Graph Embedding Algorithms¶

AE Static¶

class dynamicgem.embedding.ae_static.AE(d, *hyper_dict, **kwargs)[source]¶

Auto-Encoder based static graph embedding.

AE is a static graph embedding method which can be used as a baseline for comparing the dynamic graph embedding methods. It uses the fully connected Nueral network as its encoder and decoder.

Parameters

d (int) – dimension of the embedding
beta (float) – penalty parameter in matrix B of 2nd order objective
nu1 (float) – L1-reg hyperparameter
nu2 (float) – L2-reg hyperparameter
K (float) – number of hidden layers in encoder/decoder
n_units (list) – vector of length K-1 containing #units in hidden layers of encoder/decoder, not including the units in the embedding layer
n_iter (int) – number of sgd iterations for first embedding (const)
xeta (float) – sgd step size parameter
n_batch (int) – minibatch size for SGD
modelfile (str) – Files containing previous encoder and decoder models
weightfile (str) – Files containing previous encoder and decoder weights

Examples

>>> from dynamicgem.embedding.ae_static import AE
>>> from dynamicgem.graph_generation import dynamic_SBM_graph
>>> node_num = 1000
>>> community_num = 2
>>> node_change_num = 10
>>> length =5
>>> dynamic_sbm_series = dynamic_SBM_graph.get_community_diminish_series_v2(node_num,
                                                                        community_num,
                                                                        length,
                                                                        1,
                                                                        node_change_num)
>>> embedding = AE(d=dim_emb,
               beta=5,
               nu1=1e-6,
               nu2=1e-6,
               K=3,
               n_units=[500, 300, ],
               n_iter=epochs,
               xeta=1e-4,
               n_batch=100,
               modelfile=['./intermediate/enc_modelsbm.json',
                          './intermediate/dec_modelsbm.json'],
               weightfile=['./intermediate/enc_weightssbm.hdf5',
                           './intermediate/dec_weightssbm.hdf5'])

>>> graphs = [g[0] for g in dynamic_sbm_series]
>>> embs = []

>>> for temp_var in range(length):
>>>         emb, _ = embedding.learn_embeddings(graphs[temp_var])
>>>         embs.append(emb)

get_edge_weight(self, i, j, embed=None, filesuffix=None)[source]¶

Function to get edge weight.

i¶

source node for the edge.

Type: int

j¶

target node for the edge.

Type: int

embed¶

Embedding values of all the nodes.

Type: Matrix

filesuffix¶

File suffix to be used to load the embedding.

Type: str

Returns: Weight of the given edge.
Return type: Float

get_embedding(self, filesuffix=None)[source]¶

Function to load the embedding values.

filesuffix

File suffix to be used to load the embedding.

Type: str

Returns: Numpy vector of embedding values
Return type: Vector

get_method_name(self)[source]¶

Function to return the method name.

Returns: Name of the method.
Return type: String

get_method_summary(self)[source]¶

Function to return the summary of the algorithm.

Returns: Method summary
Return type: String

get_reconst_from_embed(self, embed, node_l=None, filesuffix=None)[source]¶

Function to reconstruct the graph from the embedding.

node_l¶

node for which the adjacency list will be created.

Type: int

embed

Embedding values of all the nodes.

Type: Matrix

filesuffix

File suffix to be used to load the embedding.

Type: str

Returns: REconstructed graph for the given nodes.
Return type: List

get_reconstructed_adj(self, embed=None, node_l=None, filesuffix=None)[source]¶

Function to reconstruct the adjacency list for the given node.

node_l

node for which the adjacency list will be created.

Type: int

embed

Embedding values of all the nodes.

Type: Matrix

filesuffix

File suffix to be used to load the embedding.

Type: str

Returns: Adjacency list of the given node.
Return type: List

learn_embeddings(self, graph=None, edge_f=None)[source]¶

Learns the embedding of the nodes.

graph¶

Networkx Graph Object

Type: Object

edge_f¶

Edge list

type

List

Returns:: List: Node embeddings and time taken by the algorithm

predict_next_adj(self, node_l=None)[source]¶

Function to predict the next adjacency for the given node.

node_l

node for which the adjacency list will be created.

Type: int

Returns: Reconstructed adjancey list.
Return type: List

DynamicAE (dyngraph2vecAE)¶

class dynamicgem.embedding.dynAE.DynAE(d, *hyper_dict, **kwargs)[source]¶

Dynamic AutoEncoder

DynAE is a dynamic graph embedding algorithm which also takes different timestep graph with varying lookback to be considered in embedding the nodes using the autoencoder.

Parameters

d (int) – dimension of the embedding
beta (float) – penalty parameter in matrix B of 2nd order objective
n_prev_graphs (int) – Lookback (number of previous graphs to be considered) for the dynamic graph embedding
nu1 (float) – L1-reg hyperparameter
nu2 (float) – L2-reg hyperparameter
K (float) – number of hidden layers in encoder/decoder
rho (float) – bounding ratio for number of units in consecutive layers (< 1)
n_units (list) – vector of length K-1 containing #units in hidden layers of encoder/decoder, not including the units in the embedding layer
n_iter (int) – number of sgd iterations for first embedding (const)
xeta (float) – sgd step size parameter
n_batch (int) – minibatch size for SGD
modelfile (str) – Files containing previous encoder and decoder models
weightfile (str) – Files containing previous encoder and decoder weights

Examples

>>> from dynamicgem.embedding.dynAE import DynAE
>>> from dynamicgem.graph_generation import dynamic_SBM_graph
>>> node_num = 1000
>>> community_num = 2
>>> node_change_num = 10
>>> length =5
>>> dynamic_sbm_series = dynamic_SBM_graph.get_community_diminish_series_v2(node_num,
                                                                        community_num,
                                                                        length,
                                                                        1,
                                                                        node_change_num)
>>> embedding = DynAE(d=dim_emb,
                beta=5,
                n_prev_graphs=lookback,
                nu1=1e-6,
                nu2=1e-6,
                n_units=[500, 300, ],
                rho=0.3,
                n_iter=epochs,
                xeta=args.learningrate,
                n_batch=args.batch,
                modelfile=['./intermediate/enc_model.json', './intermediate/dec_model.json'],
                weightfile=['./intermediate/enc_weights.hdf5', './intermediate/dec_weights.hdf5'],
                savefilesuffix="testing")

>>> graphs = [g[0] for g in dynamic_sbm_series]
>>> embs = []

>>> for temp_var in range(length):
>>>         emb, _ = embedding.learn_embeddings(graphs[temp_var])
>>>         embs.append(emb)

get_edge_weight(self, i, j, embed=None, filesuffix=None)[source]¶

Function to get edge weight.

i¶

source node for the edge.

Type: int

j¶

target node for the edge.

Type: int

embed¶

Embedding values of all the nodes.

Type: Matrix

filesuffix¶

File suffix to be used to load the embedding.

Type: str

Returns: Weight of the given edge.
Return type: Float

get_embeddings(self)[source]¶: Function to return the embeddings

get_method_name(self)[source]¶

Function to return the method name.

Returns: Name of the method.
Return type: String

get_method_summary(self)[source]¶

Function to return the summary of the algorithm.

Returns: Method summary
Return type: String

get_reconst_from_embed(self, embed, filesuffix=None)[source]¶

Function to reconstruct the graph from the embedding.

node_l¶

node for which the adjacency list will be created.

Type: int

embed

Embedding values of all the nodes.

Type: Matrix

filesuffix

File suffix to be used to load the embedding.

Type: str

Returns: REconstructed graph for the given nodes.
Return type: List

get_reconstructed_adj(self, embed=None, node_l=None, filesuffix=None)[source]¶

Function to reconstruct the adjacency list for the given node.

node_l

node for which the adjacency list will be created.

Type: int

embed

Embedding values of all the nodes.

Type: Matrix

filesuffix

File suffix to be used to load the embedding.

Type: str

Returns: Adjacency list of the given node.
Return type: List

learn_embeddings(self, graphs)[source]¶

Learns the embedding of the nodes.

graph¶

Networkx Graph Object

type

Object

Returns:: List: Node embeddings and time taken by the algorithm

predict_next_adj(self, node_l=None)[source]¶

Function to predict the next adjacency for the given node.

node_l

node for which the adjacency list will be created.

Type: int

Returns: Reconstructed adjancey list.
Return type: List

DynamicAERNN (dyngraph2vecAERNN)¶

class dynamicgem.embedding.dynAERNN.DynAERNN(d, *hyper_dict, **kwargs)[source]¶

Dynamic AutoEncoder with Recurrent Neural Network

dyngraph2vecAERNN or DynAERNN is a dynamic graph embedding algorithm which combines the auto-encoder with the recurrent neural network to perform the embedding for the temporally evolving graphs.

Parameters

d (int) – dimension of the embedding
beta (float) – penalty parameter in matrix B of 2nd order objective
n_prev_graphs (int) – Lookback (number of previous graphs to be considered) for the dynamic graph embedding
nu1 (float) – L1-reg hyperparameter
nu2 (float) – L2-reg hyperparameter
K (float) – number of hidden layers in encoder/decoder
rho (float) – bounding ratio for number of units in consecutive layers (< 1)
n_aeunits (list) –
List of embedding dimension for lstm layers (n_lstmunits=) –
n_iter (int) – number of sgd iterations for first embedding (const)
xeta (float) – sgd step size parameter
n_batch (int) – minibatch size for SGD
modelfile (str) – Files containing previous encoder and decoder models
weightfile (str) – Files containing previous encoder and decoder weights

Examples

>>> from dynamicgem.embedding.dynAERNN import DynAERNN
>>> from dynamicgem.graph_generation import dynamic_SBM_graph
>>> node_num = 1000
>>> community_num = 2
>>> node_change_num = 10
>>> length =5
>>> dynamic_sbm_series = dynamic_SBM_graph.get_community_diminish_series_v2(node_num,
                                                                        community_num,
                                                                        length,
                                                                        1,
                                                                        node_change_num)
>>> embedding = DynAERNN(d=dim_emb,
                beta=5,
                n_prev_graphs=lookback,
                nu1=1e-6,
                nu2=1e-6,
                n_units=[500, 300, ],
                rho=0.3,
                n_iter=epochs,
                xeta=args.learningrate,
                n_batch=args.batch,
                modelfile=['./intermediate/enc_model.json', './intermediate/dec_model.json'],
                weightfile=['./intermediate/enc_weights.hdf5', './intermediate/dec_weights.hdf5'],
                savefilesuffix="testing")

>>> graphs = [g[0] for g in dynamic_sbm_series]
>>> embs = []

>>> for temp_var in range(length):
>>>         emb, _ = embedding.learn_embeddings(graphs[temp_var])
>>>         embs.append(emb)

get_edge_weight(self, i, j, embed=None, filesuffix=None)[source]¶

Function to get edge weight.

i¶

source node for the edge.

Type: int

j¶

target node for the edge.

Type: int

embed¶

Embedding values of all the nodes.

Type: Matrix

filesuffix¶

File suffix to be used to load the embedding.

Type: str

Returns: Weight of the given edge.
Return type: Float

get_embeddings(self)[source]¶: Function to return the embeddings

get_method_name(self)[source]¶

Function to return the method name.

Returns: Name of the method.
Return type: String

get_method_summary(self)[source]¶

Function to return the summary of the algorithm.

Returns: Method summary
Return type: String

get_reconst_from_embed(self, embed, filesuffix=None)[source]¶

Function to reconstruct the graph from the embedding.

node_l¶

node for which the adjacency list will be created.

Type: int

embed

Embedding values of all the nodes.

Type: Matrix

filesuffix

File suffix to be used to load the embedding.

Type: str

Returns: REconstructed graph for the given nodes.
Return type: List

get_reconstructed_adj(self, embed=None, node_l=None, filesuffix=None)[source]¶

Function to reconstruct the adjacency list for the given node.

node_l

node for which the adjacency list will be created.

Type: int

embed

Embedding values of all the nodes.

Type: Matrix

filesuffix

File suffix to be used to load the embedding.

Type: str

Returns: Adjacency list of the given node.
Return type: List

learn_embeddings(self, graphs)[source]¶

Learns the embedding of the nodes.

graph¶

Networkx Graph Object

type

Object

Returns:: List: Node embeddings and time taken by the algorithm

predict_next_adj(self, node_l=None)[source]¶

Function to predict the next adjacency for the given node.

node_l

node for which the adjacency list will be created.

Type: int

Returns: Reconstructed adjancey list.
Return type: List

Dynamic TRIAD¶

class dynamicgem.embedding.dynamicTriad.dynamicTriad(*hyper_dict, **kwargs)[source]¶

Dynamic Triad Closure based embedding

DynamicTriad preserves both structural informa- tion and evolution patterns of a given network. The general idea of our approach is to impose triad, which is a group of three vertices and is one of the basic units of networks.

Parameters

niters (int) – Number of iteration to run the algorithm
starttime (int) – start time for the graph step
datafile (str) – The file for the input graph
batchsize (int) – batch size for training the algorithm
nsteps (int) – total number of steps in the temporal graph
embdim (int) – embedding dimension
stepsize (int) – step size for the graph
stepstride (int) – stride to consider for temporal stride
outdir (str) – The output directory to store the result
cachefn (str) – Directory to cache the temporary data
lr (float) – Learning rate for the algorithm
beta (float) – coefficients for triad component
negdup (float) – neg/pos ratio during sampling
datasetmod (str) – module name for dataset loading
trainmod (str) – module name for training model
pretrain_size (int) – size of the graph for pre-training
sampling_args (int) – sampling size
validation (list) – link_reconstruction validation data
datatype (str) – type of network data
scale (int) – scaling
classifier (str) – type of classifier to be used
debug (bool) – debugging flag
test (bool) – type of test to perform
repeat (int) – Number of times to repeat the learning
resultdir (str) – directory to store the result
testDataType (str) – type of test data
clname (str) – classifier type
node_num (int) – number of nodes

Examples

>>> from dynamicgem.embedding.dynamicTriad import dynamicTriad
>>> from dynamicgem.graph_generation import dynamic_SBM_graph
>>> node_num = 200
>>> community_num = 2
>>> node_change_num = 2
>>> length =5
>>> dynamic_sbm_series = dynamic_SBM_graph.get_community_diminish_series_v2(node_num,
                                                                        community_num,
                                                                        length,
                                                                        1,
                                                                        node_change_num)
>>> graphs = [g[0] for g in dynamic_sbm_series]

>>> datafile = dataprep_util.prep_input_dynTriad(graphs, length, args.testDataType)

>>> embedding = dynamicTriad(niters=10,
                         starttime=0,
                         datafile=datafile,
                         batchsize=10,
                         nsteps=5,
                         embdim=16,
                         stepsize=1,
                         stepstride=1,
                         outdir='./output',
                         cachefn='./tmp',
                         lr=0.001,
                         beta=0.1,
                         negdup=1,
                         datasetmod='dynamicgem.utils.dynamictriad_utils.dataset.adjlist',
                         trainmod='dynamicgem.utils.dynamictriad_utils.algorithm.dynamic_triad',
                         pretrain_size=4,
                         sampling_args={},
                         validation='link_reconstruction',
                         datatype='sbm_cd',
                         scale=1,
                         classifier='lr',
                         debug=False,
                         test='link_predict',
                         repeat=1,
                         resultdir='./results_link_all',
                         testDataType='sbm_cd',
                         clname='lr',
                         node_num=node_num )

>>> embedding.learn_embedding()
>>> embedding.get_embedding()
>>> outdir = args.resultdir
>>> if not os.path.exists(outdir):
>>>     os.mkdir(outdir)
>>> outdir = outdir + '/' + args.testDataType
>>> if not os.path.exists(outdir):
>>>     os.mkdir(outdir)
>>> outdir = outdir + '/' + 'dynTRIAD'
>>> if not os.path.exists(outdir):
 >>>    os.mkdir(outdir)

>>> lp.expstaticLP_TRIAD(dynamic_sbm_series,
                     graphs,
                     embedding,
                     1,
                     outdir + '/',
                     'nm' + str(args.nodemigration) + '_l' + str(args.nsteps) + '_emb' + str(args.embdim),
                     )

class ResultPresenter[source]¶: result presenter class

export(self, vertices, data, outdir)[source]¶: function to export the data

get_edge_weight(self, t, i, j)[source]¶

Function to get edge weight.

i¶

source node for the edge.

Type: int

j¶

target node for the edge.

Type: int

embed¶

Embedding values of all the nodes.

Type: Matrix

filesuffix¶

File suffix to be used to load the embedding.

Type: str

Returns: Weight of the given edge.
Return type: Float

get_embedding(self)[source]¶: Function to return the embeddings

get_method_name(self)[source]¶

Function to return the method name.

Returns: Name of the method.
Return type: String

get_method_summary(self)[source]¶

Function to return the summary of the algorithm.

Returns: Method summary
Return type: String

get_reconstructed_adj(self, t, X=None, node_l=None)[source]¶

Function to reconstruct the adjacency list for the given node.

node_l¶

node for which the adjacency list will be created.

Type: int

X¶

Embedding values of all the nodes.

Type: Matrix

t¶

Time step

Type: int

Returns: Adjacency list of the given node.
Return type: List

learn_embedding(self)[source]¶

Learns the embedding of the nodes.

Returns: Node embeddings and time taken by the algorithm
Return type: List

link_predict(self, g, t, intv=0, repeat=1)[source]¶: Function to perform link prediction

load_datamod(self, modname)[source]¶: Function to load the dataset module

load_embedding(self, fn, vs)[source]¶: Function to load the embedding

load_or_update_cache(self, ds, cachefn)[source]¶: Function to either update or load the cache

load_trainmod(self, modname)[source]¶: Function to load the training module

plotresults(self, dynamic_sbm_series)[source]¶: Function to plot the result

predict_next_adj(self, t, node_l=None)[source]¶

Function to predict the next adjacency for the given node.

node_l

node for which the adjacency list will be created.

Type: int

Returns: Reconstructed adjancey list.
Return type: List

sample_link_reconstruction(self, g, sample_nodes=None, negdup=1)[source]¶: Function to sample the link reconstruction

Dynamic GEM (dynGEM)¶

class dynamicgem.embedding.dynGEM.DynGEM(*hyper_dict, **kwargs)[source]¶

Structural Deep Network Embedding

DynSDNE (also DynGEM) perfomr the dynamic network embedding while utilizing Structural Deep Network Embedding (SDNE) with dynamically evolving graphs as input.

Parameters

d (int) – dimension of the embedding
beta (float) – penalty parameter in matrix B of 2nd order objective
n_prev_graphs (int) – Lookback (number of previous graphs to be considered) for the dynamic graph embedding
nu1 (float) – L1-reg hyperparameter
nu2 (float) – L2-reg hyperparameter
K (float) – number of hidden layers in encoder/decoder
rho (float) – bounding ratio for number of units in consecutive layers (< 1)
n_aeunits (list) –
List of embedding dimension for lstm layers (n_lstmunits=) –
n_iter (int) – number of sgd iterations for first embedding (const)
xeta (float) – sgd step size parameter
n_batch (int) – minibatch size for SGD
modelfile (str) – Files containing previous encoder and decoder models
weightfile (str) – Files containing previous encoder and decoder weights

Examples

>>> from dynamicgem.embedding.dynSDNE import DynSDNE
>>> from dynamicgem.graph_generation import dynamic_SBM_graph
>>> node_num = 1000
>>> community_num = 2
>>> node_change_num = 10
>>> length =5
>>> dynamic_sbm_series = dynamic_SBM_graph.get_community_diminish_series_v2(node_num,
                                                                        community_num,
                                                                        length,
                                                                        1,
                                                                        node_change_num)
>>> graphs = [g[0] for g in dynamic_sbm_series]
>>> embedding = DynSDNE(d=128, beta=5, alpha=0, nu1=1e-6, nu2=1e-6, K=3,
             n_units=[500, 300], n_iter=20, xeta=0.01,
             n_batch=500,
             modelfile=['./intermediate/enc_model.json',
                        './intermediate/dec_model.json'],
             weightfile=['./intermediate/enc_weights.hdf5',
                         './intermediate/dec_weights.hdf5'])
>>> embedding.learn_embedding(graph=graphs._graph, edge_f=None,
                      is_weighted=True, no_python=True)

get_edge_weight(self, i, j, embed=None, filesuffix=None)[source]¶

Function to get edge weight.

i¶

source node for the edge.

Type: int

j¶

target node for the edge.

Type: int

embed¶

Embedding values of all the nodes.

Type: Matrix

filesuffix¶

File suffix to be used to load the embedding.

Type: str

Returns: Weight of the given edge.
Return type: Float

get_embedding(self, filesuffix=None)[source]¶: Function to return the embeddings

get_method_name(self)[source]¶

Function to return the method name.

Returns: Name of the method.
Return type: String

get_method_summary(self)[source]¶

Function to return the summary of the algorithm.

Returns: Method summary
Return type: String

get_reconst_from_embed(self, embed, node_l=None, filesuffix=None)[source]¶

Function to reconstruct the graph from the embedding.

node_l¶

node for which the adjacency list will be created.

Type: int

embed

Embedding values of all the nodes.

Type: Matrix

filesuffix

File suffix to be used to load the embedding.

Type: str

Returns: REconstructed graph for the given nodes.
Return type: List

get_reconstructed_adj(self, embed=None, node_l=None, filesuffix=None)[source]¶

Function to reconstruct the adjacency list for the given node.

node_l

node for which the adjacency list will be created.

Type: int

embed

Embedding values of all the nodes.

Type: Matrix

filesuffix

File suffix to be used to load the embedding.

Type: str

Returns: Adjacency list of the given node.
Return type: List

learn_embedding(self, graph=None, edge_f=None, is_weighted=False, no_python=False)[source]¶

Learns the embedding of the nodes.

graph¶

Networkx Graph Object

type

Object

Returns:: List: Node embeddings and time taken by the algorithm

Dynamic RNN (dyngraph2vecRNN)¶

class dynamicgem.embedding.dynRNN.DynRNN(d, *hyper_dict, **kwargs)[source]¶

Dynamic embedding with Recurrent Neural Network

dyngraph2vecRNN or DynRNN is a dynamic graph embedding algorithm which uses the recurrent neural network to perform the embedding for the temporally evolving graphs.

Parameters

d (int) – dimension of the embedding
beta (float) – penalty parameter in matrix B of 2nd order objective
n_prev_graphs (int) – Lookback (number of previous graphs to be considered) for the dynamic graph embedding
nu1 (float) – L1-reg hyperparameter
nu2 (float) – L2-reg hyperparameter
K (float) – number of hidden layers in encoder/decoder
rho (float) – bounding ratio for number of units in consecutive layers (< 1)
n_enc_units (list) –
n_dec_units (list) –
n_iter (int) – number of sgd iterations for first embedding (const)
xeta (float) – sgd step size parameter
n_batch (int) – minibatch size for SGD
modelfile (str) – Files containing previous encoder and decoder models
weightfile (str) – Files containing previous encoder and decoder weights

Examples

>>> from dynamicgem.embedding.dynRNN import DynRNN
>>> from dynamicgem.graph_generation import dynamic_SBM_graph
>>> node_num = 1000
>>> community_num = 2
>>> node_change_num = 10
>>> length =5
>>> dynamic_sbm_series = dynamic_SBM_graph.get_community_diminish_series_v2(node_num,
                                                                        community_num,
                                                                        length,
                                                                        1,
                                                                        node_change_num)
>>> embedding = DynRNN(d=dim_emb,
                beta=5,
                n_prev_graphs=lookback,
                nu1=1e-6,
                nu2=1e-6,
                n_units=[500, 300, ],
                rho=0.3,
                n_iter=epochs,
                xeta=args.learningrate,
                n_batch=args.batch,
                modelfile=['./intermediate/enc_model.json', './intermediate/dec_model.json'],
                weightfile=['./intermediate/enc_weights.hdf5', './intermediate/dec_weights.hdf5'],
                savefilesuffix="testing")

>>> graphs = [g[0] for g in dynamic_sbm_series]
>>> embs = []

>>> for temp_var in range(length):
>>>         emb, _ = embedding.learn_embeddings(graphs[temp_var])
>>>         embs.append(emb)

get_edge_weight(self, i, j, embed=None, filesuffix=None)[source]¶

Function to get edge weight.

i¶

source node for the edge.

Type: int

j¶

target node for the edge.

Type: int

embed¶

Embedding values of all the nodes.

Type: Matrix

filesuffix¶

File suffix to be used to load the embedding.

Type: str

Returns: Weight of the given edge.
Return type: Float

get_embeddings(self)[source]¶: Function to return the embeddings

get_method_name(self)[source]¶

Function to return the method name.

Returns: Name of the method.
Return type: String

get_method_summary(self)[source]¶

Function to return the summary of the algorithm.

Returns: Method summary
Return type: String

get_reconst_from_embed(self, embed, filesuffix=None)[source]¶

Function to reconstruct the graph from the embedding.

node_l¶

node for which the adjacency list will be created.

Type: int

embed

Embedding values of all the nodes.

Type: Matrix

filesuffix

File suffix to be used to load the embedding.

Type: str

Returns: REconstructed graph for the given nodes.
Return type: List

get_reconstructed_adj(self, embed=None, node_l=None, filesuffix=None)[source]¶

Function to reconstruct the adjacency list for the given node.

node_l

node for which the adjacency list will be created.

Type: int

embed

Embedding values of all the nodes.

Type: Matrix

filesuffix

File suffix to be used to load the embedding.

Type: str

Returns: Adjacency list of the given node.
Return type: List

learn_embeddings(self, graphs)[source]¶

Learns the embedding of the nodes.

graph¶

Networkx Graph Object

type

Object

Returns:: List: Node embeddings and time taken by the algorithm

predict_next_adj(self, node_l=None)[source]¶

Function to predict the next adjacency for the given node.

node_l

node for which the adjacency list will be created.

Type: int

Returns: Reconstructed adjancey list.
Return type: List

Dynamic Graph Factorization¶

class dynamicgem.embedding.graphFac_dynamic.GraphFactorization(d, n_iter, n_iter_sub, eta, regu, kappa, initEmbed=None)[source]¶

Graph Facgorization based network embedding

It utilizes factorization based method to acquire the embedding of the graph nodes.

Parameters

d (int) – dimension of the embedding
eta (float) – learning rate of sgd
regu (float) – regularization coefficient of magnitude of weights
beta (float) – penalty parameter in matrix B of 2nd order objective
n_iter (int) – number of sgd iterations for first embedding (const)
method_name (str) – method name
initEmbed (Matrix) – Previous timestep embedding initialized for the current timestep

Examples

>>> from dynamicgem.embedding.graphFac_dynamic import GraphFactorization
>>> from dynamicgem.graph_generation import dynamic_SBM_graph
>>> node_num = 100
>>> community_num = 2
>>> node_change_num = 2
>>> length = 5
>>> dynamic_sbm_series = dynamic_SBM_graph.get_random_perturbation_series_v2(node_num, community_num, length,
                                                                      node_change_num)
>>> dynamic_embeddings = GraphFactorization(100, 100, 10, 5 * 10 ** -2, 1.0, 1.0)
>>> dynamic_embeddings.learn_embeddings([g[0] for g in dynamic_sbm_series])

>>> plot_dynamic_sbm_embedding.plot_dynamic_sbm_embedding(dynamic_embeddings.get_embeddings(), dynamic_sbm_series)
>>> plt.show()

getFVal(self, adj_mtx, X, prev_step_emb=None)[source]¶: Function to Factorize the adjacency matrix.

get_edge_weight(self, i, j)[source]¶

Function to get edge weight.

i¶

source node for the edge.

Type: int

j¶

target node for the edge.

Type: int

embed¶

Embedding values of all the nodes.

Type: Matrix

filesuffix¶

File suffix to be used to load the embedding.

Type: str

Returns: Weight of the given edge.
Return type: Float

get_embedding(self)[source]¶: Function to return a single graph embedding

get_embeddings(self)[source]¶: Function to return the whole tempral graphs embeddings

get_method_name(self)[source]¶

Function to return the method name.

Returns: Name of the method.
Return type: String

get_method_summary(self)[source]¶

Function to return the summary of the algorithm.

Returns: Method summary
Return type: String

get_reconstructed_adj(self, X=None)[source]¶

Function to reconstruct the adjacency list for the given node.

node_l¶

node for which the adjacency list will be created.

Type: int

embed

Embedding values of all the nodes.

Type: Matrix

filesuffix

File suffix to be used to load the embedding.

Type: str

Returns: Adjacency list of the given node.
Return type: List

learn_embedding(self, graph, prevEmbed=None)[source]¶

Learns the embedding of the nodes.

graph¶

Networkx Graph Object

type

Object

Returns:: List: Node embeddings and time taken by the algorithm

learn_embeddings(self, graphs, prevStepInfo=False)[source]¶: Learning the graph embedding from the adjcency matrix. :param graphs: the graphs to embed in networkx DiGraph format

Dynamic SDNE¶

class dynamicgem.embedding.sdne_dynamic.SDNE(d, beta, alpha, nu1, nu2, K, n_units, rho, n_iter, n_iter_subs, xeta, n_batch, modelfile=None, weightfile=None, node_frac=1, n_walks_per_node=5, len_rw=2)[source]¶

Structural Deep Network Embedding

SDNE perform the network embedding while utilizing Structural Deep Network Embedding (SDNE).

Parameters

d (int) – dimension of the embedding
beta (float) – penalty parameter in matrix B of 2nd order objective
n_prev_graphs (int) – Lookback (number of previous graphs to be considered) for the dynamic graph embedding
nu1 (float) – L1-reg hyperparameter
nu2 (float) – L2-reg hyperparameter
K (float) – number of hidden layers in encoder/decoder
rho (float) – bounding ratio for number of units in consecutive layers (< 1)
n_aeunits (list) –
List of embedding dimension for lstm layers (n_lstmunits=) –
n_iter (int) – number of sgd iterations for first embedding (const)
xeta (float) – sgd step size parameter
n_batch (int) – minibatch size for SGD
modelfile (str) – Files containing previous encoder and decoder models
weightfile (str) – Files containing previous encoder and decoder weights
node_frac (float) – Fraction of nodes to use for random walk
n_walks_per_node (int) – Number of random walks to do for each selected nodes
len_rw (int) – Length of every random walk

Examples

>>> from dynamicgem.embedding.sdne_dynamic import SDNE
>>> from dynamicgem.graph_generation import dynamic_SBM_graph
>>> node_num = 100
>>> community_num = 2
>>> node_change_num = 2
>>> length = 2
>>> dynamic_sbm_series = dynamic_SBM_graph.get_community_diminish_series(node_num, community_num, length, 1, node_change_num)
>>> dynamic_embedding = SDNE(d=16, beta=5, alpha=1e-5, nu1=1e-6, nu2=1e-6, K=3, n_units=[500, 300,], rho=0.3, n_iter=2, n_iter_subs=5, xeta=0.01, n_batch=500, modelfile=['./intermediate/enc_model.json', './intermediate/dec_model.json'], weightfile=['./intermediate/enc_weights.hdf5', './intermediate/dec_weights.hdf5'], node_frac=1, n_walks_per_node=10, len_rw=2)
>>> dynamic_embedding.learn_embeddings([g[0] for g in dynamic_sbm_series], False, subsample=False)
>>> plot_dynamic_sbm_embedding.plot_dynamic_sbm_embedding(dynamic_embedding.get_embeddings(), dynamic_sbm_series)
>>> plt.savefig('result/visualization_sdne_cd.png')
>>> plt.show()

get_edge_weight(self, i, j, embed=None, filesuffix=None)[source]¶

Function to get edge weight.

i¶

source node for the edge.

Type: int

j¶

target node for the edge.

Type: int

embed¶

Embedding values of all the nodes.

Type: Matrix

filesuffix¶

File suffix to be used to load the embedding.

Type: str

Returns: Weight of the given edge.
Return type: Float

get_embedding(self, filesuffix)[source]¶: Function to return sinfle embedding

get_embeddings(self)[source]¶: Function to return all the embeddings

get_method_name(self)[source]¶

Function to return the method name.

Returns: Name of the method.
Return type: String

get_method_summary(self)[source]¶

Function to return the summary of the algorithm.

Returns: Method summary
Return type: String

get_reconst_from_embed(self, embed, filesuffix=None)[source]¶

Function to reconstruct the graph from the embedding.

node_l¶

node for which the adjacency list will be created.

Type: int

embed

Embedding values of all the nodes.

Type: Matrix

filesuffix

File suffix to be used to load the embedding.

Type: str

Returns: REconstructed graph for the given nodes.
Return type: List

get_reconstructed_adj(self, embed=None, filesuffix=None)[source]¶

Function to reconstruct the adjacency list for the given node.

node_l

node for which the adjacency list will be created.

Type: int

embed

Embedding values of all the nodes.

Type: Matrix

filesuffix

File suffix to be used to load the embedding.

Type: str

Returns: Adjacency list of the given node.
Return type: List

learn_embedding(self, graph, prevStepInfo=True, loadfilesuffix=None, savefilesuffix=None, subsample=False)[source]¶

Learns the embedding of the nodes.

graph¶

Networkx Graph Object

Type: Object

prevStepInfo¶

Incorporate previous step info

Type: bool

loadfilesuffix¶

file suffix for loading the previos data

Type: str

savefilesuffix¶

file suffix to be used for saving the data

type

str

Returns:: List: Node embeddings and time taken by the algorithm

learn_embeddings(self, graphs, prevStepInfo=False, loadsuffixinfo=None, savesuffixinfo=None, subsample=False)[source]¶

Learns the embedding of the nodes.

graph

Networkx Graph Object

Type: Object

prevStepInfo

Incorporate previous step info

Type: bool

loadfilesuffix

file suffix for loading the previos data

Type: str

savefilesuffix

file suffix to be used for saving the data

type

str

Returns:: List: Node embeddings and time taken by the algorithm

TIMERS¶

class dynamicgem.embedding.TIMERS.TIMERS(*hyper_dict, **kwargs)[source]¶

Timers: Dynamic graph embedding

Timers perfomrs dynamic graph embedding by utilizing the SVDS decomposition of incremental graph.

Parameters: Args – K (int): dimension of the embedding theta (float): threshold for rerun datafile (str): location of the data file length (int) : total timesteps of the data nodemigraiton (int): number of nodes to migrate for sbm_cd datatype resultdir (str): directory to save the result datatype (str): sbm_cd, enron, academia, hep, AS

Examples

>>> from dynamicgem.embedding.TIMERS import TIMERS
>>> from dynamicgem.graph_generation import dynamic_SBM_graph
>>> node_num = 100
>>> community_num = 2
>>> node_change_num = 2
>>> length =5
>>> resultdir='./results_link_all'
>>> dynamic_sbm_series = dynamic_SBM_graph.get_community_diminish_series_v2(node_num,
                                                                        community_num,
                                                                        length,
                                                                        1,
                                                                        node_change_num)
>>> graphs = [g[0] for g in dynamic_sbm_series]

>>> datafile = dataprep_util.prep_input_TIMERS(graphs, length, args.testDataType)

>>> embedding = TIMERS(K=16,
                   Theta=0.5,
                   datafile=datafile,
                   length=length,
                   nodemigration=node_change_num,
                   resultdir=resultdir,
                   datatype='sbm_cd'
                   )
>>> outdir_tmp = './output'
>>> if not os.path.exists(outdir_tmp):
>>>     os.mkdir(outdir_tmp)
>>> outdir_tmp = outdir_tmp + '/sbm_cd'
>>> if not os.path.exists(outdir_tmp):
>>>    os.mkdir(outdir_tmp)
>>> if not os.path.exists(outdir_tmp + '/incrementalSVD'):
>>>    os.mkdir(outdir_tmp + '/incrementalSVD')
>>> if not os.path.exists(outdir_tmp + '/rerunSVD'):
>>>     os.mkdir(outdir_tmp + '/rerunSVD')
>>> if not os.path.exists(outdir_tmp + '/optimalSVD'):
>>>     os.mkdir(outdir_tmp + '/optimalSVD')

>>>  embedding.learn_embedding()

>>>  outdir = resultdir
>>>  if not os.path.exists(outdir):
>>>     os.mkdir(outdir)
>>>  outdir = outdir + '/' + args.testDataType
>>>  if not os.path.exists(outdir):
>>>      os.mkdir(outdir)

>>>  embedding.get_embedding(outdir_tmp, 'incrementalSVD')
     # embedding.plotresults()
>>>  outdir1 = outdir + '/incrementalSVD'
>>>  if not os.path.exists(outdir1):
>>>      os.mkdir(outdir1)
>>>  lp.expstaticLP_TIMERS(dynamic_sbm_series,
                          graphs,
                          embedding,
                          1,
                          outdir1 + '/',
                          'nm' + str(args.nodemigration) + '_l' + str(length) + '_emb' + str(int(dim_emb)),
                          )

>>>  embedding.get_embedding(outdir_tmp, 'rerunSVD')
>>>  outdir1 = outdir + '/rerunSVD'
    # embedding.plotresults()
>>>  if not os.path.exists(outdir1):
>>>      os.mkdir(outdir1)
>>>  lp.expstaticLP_TIMERS(dynamic_sbm_series,
                          graphs,
                          embedding,
                          1,
                          outdir1 + '/',
                          'nm' + str(args.nodemigration) + '_l' + str(length) + '_emb' + str(int(dim_emb)),
                          )

>>>  embedding.get_embedding(outdir_tmp, 'optimalSVD')
    # embedding.plotresults()
>>>  outdir1 = outdir + '/optimalSVD'
>>>  if not os.path.exists(outdir1):
>>>      os.mkdir(outdir1)
>>>   lp.expstaticLP_TIMERS(dynamic_sbm_series,
                          graphs,
                          embedding,
                          1,
                          outdir1 + '/',
                          'nm' + str(args.nodemigration) + '_l' + str(length) + '_emb' + str(int(dim_emb)),
                          )

get_edge_weight(self, t, i, j)[source]¶

Function to get edge weight.

i¶

source node for the edge.

Type: int

j¶

target node for the edge.

Type: int

embed¶

Embedding values of all the nodes.

Type: Matrix

filesuffix¶

File suffix to be used to load the embedding.

Type: str

Returns: Weight of the given edge.
Return type: Float

get_embedding(self, outdir_tmp, method)[source]¶: Function to return the embeddings

get_method_name(self)[source]¶

Function to return the method name.

Returns: Name of the method.
Return type: String

get_method_summary(self)[source]¶

Function to return the summary of the algorithm.

Returns: Method summary
Return type: String

get_reconstructed_adj(self, t, X=None, node_l=None)[source]¶

Function to reconstruct the adjacency list for the given node.

node_l¶

node for which the adjacency list will be created.

Type: int

embed

Embedding values of all the nodes.

Type: Matrix

filesuffix

File suffix to be used to load the embedding.

Type: str

Returns: Adjacency list of the given node.
Return type: List

learn_embedding(self, graph=None)[source]¶

Learns the embedding of the nodes.

graph¶

Networkx Graph Object

type

Object

Returns:: List: Node embeddings and time taken by the algorithm

plotresults(self, dynamic_sbm_series)[source]¶: Function to plot the results

predict_next_adj(self, t, node_l=None)[source]¶

Function to predict the next adjacency for the given node.

node_l

node for which the adjacency list will be created.

Type: int

Returns: Reconstructed adjancey list.
Return type: List

incremental SVD¶

class dynamicgem.embedding.incrementalSVD.incSVD(*hyper_dict, **kwargs)[source]¶

Incremental Singular Value Decomposition

Utilizes the incremental SVD decomposition to acquire the embedding of the nodes.

Parameters: Args – K (int): dimension of the embedding theta (float): threshold for rerun datafile (str): location of the data file length (int) : total timesteps of the data nodemigraiton (int): number of nodes to migrate for sbm_cd datatype resultdir (str): directory to save the result datatype (str): sbm_cd, enron, academia, hep, AS

Examples

>>> from dynamicgem.embedding.incrementalSVD import incSVD
>>> from dynamicgem.graph_generation import dynamic_SBM_graph
>>> node_num = 100
>>> community_num = 2
>>> node_change_num = 2
>>> length =5
>>> resultdir='./results_link_all'
>>> dynamic_sbm_series = dynamic_SBM_graph.get_community_diminish_series_v2(node_num,
                                                                        community_num,
                                                                        length,
                                                                        1,
                                                                        node_change_num)
>>> graphs = [g[0] for g in dynamic_sbm_series]

>>> datafile = dataprep_util.prep_input_TIMERS(graphs, length, args.testDataType)

>>> embedding = incSVD(K=16,
                   Theta=0.5,
                   datafile=datafile,
                   length=length,
                   nodemigration=node_change_num,
                   resultdir=resultdir,
                   datatype='sbm_cd'
                   )
>>> outdir_tmp = './output'
>>> if not os.path.exists(outdir_tmp):
>>>     os.mkdir(outdir_tmp)
>>> outdir_tmp = outdir_tmp + '/sbm_cd'
>>> if not os.path.exists(outdir_tmp):
>>>    os.mkdir(outdir_tmp)
>>> if not os.path.exists(outdir_tmp + '/incrementalSVD'):
>>>    os.mkdir(outdir_tmp + '/incrementalSVD')
>>>  embedding.learn_embedding()
>>>  outdir = resultdir
>>>  if not os.path.exists(outdir):
>>>     os.mkdir(outdir)
>>>  outdir = outdir + '/' + args.testDataType
>>>  if not os.path.exists(outdir):
>>>      os.mkdir(outdir)
>>>  embedding.get_embedding(outdir_tmp, 'incrementalSVD')
     # embedding.plotresults()
>>>  outdir1 = outdir + '/incrementalSVD'
>>>  if not os.path.exists(outdir1):
>>>      os.mkdir(outdir1)
>>>  lp.expstaticLP_TIMERS(dynamic_sbm_series,
                          graphs,
                          embedding,
                          1,
                          outdir1 + '/',
                          'nm' + str(args.nodemigration) + '_l' + str(length) + '_emb' + str(int(dim_emb)),
                          )

get_edge_weight(self, t, i, j)[source]¶

Function to get edge weight.

i¶

source node for the edge.

Type: int

j¶

target node for the edge.

Type: int

embed¶

Embedding values of all the nodes.

Type: Matrix

filesuffix¶

File suffix to be used to load the embedding.

Type: str

Returns: Weight of the given edge.
Return type: Float

get_embedding(self, outdir_tmp, method)[source]¶: Function to return the embeddings

get_method_name(self)[source]¶

Function to return the method name.

Returns: Name of the method.
Return type: String

get_method_summary(self)[source]¶

Function to return the summary of the algorithm.

Returns: Method summary
Return type: String

get_reconstructed_adj(self, t, X=None, node_l=None)[source]¶

Function to reconstruct the adjacency list for the given node.

node_l¶

node for which the adjacency list will be created.

Type: int

embed

Embedding values of all the nodes.

Type: Matrix

filesuffix

File suffix to be used to load the embedding.

Type: str

Returns: Adjacency list of the given node.
Return type: List

learn_embedding(self, graph=None)[source]¶

Learns the embedding of the nodes.

graph¶

Networkx Graph Object

type

Object

Returns:: List: Node embeddings and time taken by the algorithm

plotresults(self, dynamic_sbm_series)[source]¶: Function to plot the results

predict_next_adj(self, t, node_l=None)[source]¶

Function to predict the next adjacency for the given node.

node_l

node for which the adjacency list will be created.

Type: int

Returns: Reconstructed adjancey list.
Return type: List

rerunSVD¶

class dynamicgem.embedding.rerunSVD.rerunSVD(*hyper_dict, **kwargs)[source]¶

Timers: rerun Singular Value Decomposition

Timers perfomrs dynamic graph embedding by utilizing the SVDS decomposition of incremental graph with a bound to trigger the update.

Parameters: Args – K (int): dimension of the embedding theta (float): threshold for rerun datafile (str): location of the data file length (int) : total timesteps of the data nodemigraiton (int): number of nodes to migrate for sbm_cd datatype resultdir (str): directory to save the result datatype (str): sbm_cd, enron, academia, hep, AS

Examples

>>> from dynamicgem.embedding.rerunSVD import rerunSVD
>>> from dynamicgem.graph_generation import dynamic_SBM_graph
>>> node_num = 100
>>> community_num = 2
>>> node_change_num = 2
>>> length =5
>>> resultdir='./results_link_all'
>>> dynamic_sbm_series = dynamic_SBM_graph.get_community_diminish_series_v2(node_num,
                                                                        community_num,
                                                                        length,
                                                                        1,
                                                                        node_change_num)
>>> graphs = [g[0] for g in dynamic_sbm_series]

>>> datafile = dataprep_util.prep_input_TIMERS(graphs, length, args.testDataType)

>>> embedding = rerunSVD(K=16,
                   Theta=0.5,
                   datafile=datafile,
                   length=length,
                   nodemigration=node_change_num,
                   resultdir=resultdir,
                   datatype='sbm_cd'
                   )
>>> outdir_tmp = './output'
>>> if not os.path.exists(outdir_tmp):
>>>     os.mkdir(outdir_tmp)
>>> outdir_tmp = outdir_tmp + '/sbm_cd'
>>> if not os.path.exists(outdir_tmp):
>>>    os.mkdir(outdir_tmp)
>>> if not os.path.exists(outdir_tmp + '/rerunSVD'):
>>>     os.mkdir(outdir_tmp + '/rerunSVD')
>>>  embedding.learn_embedding()
>>>  outdir = resultdir
>>>  if not os.path.exists(outdir):
>>>     os.mkdir(outdir)
>>>  outdir = outdir + '/' + args.testDataType
>>>  if not os.path.exists(outdir):
>>>      os.mkdir(outdir)
>>>  embedding.get_embedding(outdir_tmp, 'rerunSVD')
>>>  outdir1 = outdir + '/rerunSVD'
    # embedding.plotresults()
>>>  if not os.path.exists(outdir1):
>>>      os.mkdir(outdir1)
>>>  lp.expstaticLP_TIMERS(dynamic_sbm_series,
                          graphs,
                          embedding,
                          1,
                          outdir1 + '/',
                          'nm' + str(args.nodemigration) + '_l' + str(length) + '_emb' + str(int(dim_emb)),
                          )

get_edge_weight(self, t, i, j)[source]¶

Function to get edge weight.

i¶

source node for the edge.

Type: int

j¶

target node for the edge.

Type: int

embed¶

Embedding values of all the nodes.

Type: Matrix

filesuffix¶

File suffix to be used to load the embedding.

Type: str

Returns: Weight of the given edge.
Return type: Float

get_embedding(self, outdir_tmp, method)[source]¶: Function to return the embeddings

get_method_name(self)[source]¶

Function to return the method name.

Returns: Name of the method.
Return type: String

get_method_summary(self)[source]¶

Function to return the summary of the algorithm.

Returns: Method summary
Return type: String

get_reconstructed_adj(self, t, X=None, node_l=None)[source]¶

Function to reconstruct the adjacency list for the given node.

node_l¶

node for which the adjacency list will be created.

Type: int

embed

Embedding values of all the nodes.

Type: Matrix

filesuffix

File suffix to be used to load the embedding.

Type: str

Returns: Adjacency list of the given node.
Return type: List

learn_embedding(self, graph=None)[source]¶

Learns the embedding of the nodes.

graph¶

Networkx Graph Object

type

Object

Returns:: List: Node embeddings and time taken by the algorithm

plotresults(self, dynamic_sbm_series)[source]¶: Function to plot the results

predict_next_adj(self, t, node_l=None)[source]¶

Function to predict the next adjacency for the given node.

node_l

node for which the adjacency list will be created.

Type: int

Returns: Reconstructed adjancey list.
Return type: List

optimalSVD¶

class dynamicgem.embedding.optimalSVD.optimalSVD(*hyper_dict, **kwargs)[source]¶

Optimal Singular Value Decomposition

It performs the SVD of each new graph.

Parameters: Args – K (int): dimension of the embedding theta (float): threshold for rerun datafile (str): location of the data file length (int) : total timesteps of the data nodemigraiton (int): number of nodes to migrate for sbm_cd datatype resultdir (str): directory to save the result datatype (str): sbm_cd, enron, academia, hep, AS

Examples

>>> from dynamicgem.embedding.optimalSVD import optimalSVD
>>> from dynamicgem.graph_generation import dynamic_SBM_graph
>>> node_num = 100
>>> community_num = 2
>>> node_change_num = 2
>>> length =5
>>> resultdir='./results_link_all'
>>> dynamic_sbm_series = dynamic_SBM_graph.get_community_diminish_series_v2(node_num,
                                                                        community_num,
                                                                        length,
                                                                        1,
                                                                        node_change_num)
>>> graphs = [g[0] for g in dynamic_sbm_series]

>>> datafile = dataprep_util.prep_input_TIMERS(graphs, length, args.testDataType)

>>> embedding = optimalSVD(K=16,
                   Theta=0.5,
                   datafile=datafile,
                   length=length,
                   nodemigration=node_change_num,
                   resultdir=resultdir,
                   datatype='sbm_cd'
                   )
>>> outdir_tmp = './output'
>>> if not os.path.exists(outdir_tmp):
>>>     os.mkdir(outdir_tmp)
>>> outdir_tmp = outdir_tmp + '/sbm_cd'
>>> if not os.path.exists(outdir_tmp):
>>>    os.mkdir(outdir_tmp)
>>> if not os.path.exists(outdir_tmp + '/optimalSVD'):
>>>     os.mkdir(outdir_tmp + '/optimalSVD')
>>>  embedding.learn_embedding()
>>>  outdir = resultdir
>>>  if not os.path.exists(outdir):
>>>     os.mkdir(outdir)
>>>  outdir = outdir + '/' + args.testDataType
>>>  if not os.path.exists(outdir):
>>>      os.mkdir(outdir)
>>>  embedding.get_embedding(outdir_tmp, 'optimalSVD')
    # embedding.plotresults()
>>>  outdir1 = outdir + '/optimalSVD'
>>>  if not os.path.exists(outdir1):
>>>      os.mkdir(outdir1)
>>>   lp.expstaticLP_TIMERS(dynamic_sbm_series,
                          graphs,
                          embedding,
                          1,
                          outdir1 + '/',
                          'nm' + str(args.nodemigration) + '_l' + str(length) + '_emb' + str(int(dim_emb)),
                          )

get_edge_weight(self, t, i, j)[source]¶

Function to get edge weight.

i¶

source node for the edge.

Type: int

j¶

target node for the edge.

Type: int

embed¶

Embedding values of all the nodes.

Type: Matrix

filesuffix¶

File suffix to be used to load the embedding.

Type: str

Returns: Weight of the given edge.
Return type: Float

get_embedding(self, outdir_tmp, method)[source]¶: Function to return the embeddings

get_method_name(self)[source]¶

Function to return the method name.

Returns: Name of the method.
Return type: String

get_method_summary(self)[source]¶

Function to return the summary of the algorithm.

Returns: Method summary
Return type: String

get_reconstructed_adj(self, t, X=None, node_l=None)[source]¶

Function to reconstruct the adjacency list for the given node.

node_l¶

node for which the adjacency list will be created.

Type: int

embed

Embedding values of all the nodes.

Type: Matrix

filesuffix

File suffix to be used to load the embedding.

Type: str

Returns: Adjacency list of the given node.
Return type: List

learn_embedding(self, graph=None)[source]¶

Learns the embedding of the nodes.

graph¶

Networkx Graph Object

type

Object

Returns:: List: Node embeddings and time taken by the algorithm

plotresults(self, dynamic_sbm_series)[source]¶: Function to plot the results

predict_next_adj(self, t, node_l=None)[source]¶

Function to predict the next adjacency for the given node.

node_l

node for which the adjacency list will be created.

Type: int

Returns: Reconstructed adjancey list.
Return type: List

Evaluation Functions¶

Graph Reconstruction¶

dynamicgem.evaluation.evaluate_graph_reconstruction.evaluateStaticGraphReconstruction(digraph, graph_embedding, X_stat, node_l=None, sample_ratio_e=None, file_suffix=None, is_undirected=True, is_weighted=False)[source]¶

Function to evaluate static graph reconstruction

dynamicgem.evaluation.evaluate_graph_reconstruction.digraph¶

Networkx Graph Object

Type: Object

dynamicgem.evaluation.evaluate_graph_reconstruction.graph_embedding¶

Algorithm for learning graph embedding

Type: object

dynamicgem.evaluation.evaluate_graph_reconstruction.X_stat¶

Embedding values of the graph.

Type: ndarray

dynamicgem.evaluation.evaluate_graph_reconstruction.node_l¶

Total number of nodes.

Type: int

dynamicgem.evaluation.evaluate_graph_reconstruction.sammple_ratio_e¶

SAmpling ration for testing. Only sample number of nodes are tested.

Type: float

dynamicgem.evaluation.evaluate_graph_reconstruction.file_suffix¶

Suffix for file name.

Type: str

dynamicgem.evaluation.evaluate_graph_reconstruction.is_undirected¶

Flag to denote if the graph is directed.

Type: bool

dynamicgem.evaluation.evaluate_graph_reconstruction.is_weighted¶

Flag denoting if the graph has weighted edge.

type

bool

Returns:: ndarray: MAP, precision curve, error values and error baselines

dynamicgem.evaluation.evaluate_graph_reconstruction.expGR(digraph, graph_embedding, X, n_sampled_nodes, rounds, res_pre, m_summ, file_suffix=None, is_undirected=True, sampling_scheme='rw')[source]¶

Function to evaluate graph reconstruction

dynamicgem.evaluation.evaluate_graph_reconstruction.digraph

Networkx Graph Object

Type: Object

dynamicgem.evaluation.evaluate_graph_reconstruction.graph_embedding

Algorithm for learning graph embedding

Type: object

dynamicgem.evaluation.evaluate_graph_reconstruction.X_stat

Embedding values of the graph.

Type: ndarray

dynamicgem.evaluation.evaluate_graph_reconstruction.n_sampled_nodes¶

Total number of nodes.

Type: int

dynamicgem.evaluation.evaluate_graph_reconstruction.rounds¶

Number of times to run the experiment

Type: int

dynamicgem.evaluation.evaluate_graph_reconstruction.res_pre¶

prefix to be used to store the result.

Type: str

dynamicgem.evaluation.evaluate_graph_reconstruction.m_summ¶

summary to be used to save the result.

Type: str

dynamicgem.evaluation.evaluate_graph_reconstruction.file_suffix

Suffix for file name.

Type: str

dynamicgem.evaluation.evaluate_graph_reconstruction.is_undirected

Flag to denote if the graph is directed.

Type: bool

dynamicgem.evaluation.evaluate_graph_reconstruction.sampling_scheme¶

sampling scheme for selecting the nodes.

type

str

Returns:: ndarray: Mean Average precision

Link Prediction¶

dynamicgem.evaluation.evaluate_link_prediction.evaluateDynamicLinkPrediction(graph, embedding, rounds, n_sample_nodes=None, no_python=False, is_undirected=True, sampling_scheme='u_rand')[source]¶

Function to evaluate Dynamic Link Prediction

dynamicgem.evaluation.evaluate_link_prediction.graph¶

Networkx Graph Object

Type: Object

dynamicgem.evaluation.evaluate_link_prediction.embedding¶

Algorithm for learning graph embedding

Type: object

dynamicgem.evaluation.evaluate_link_prediction.n_sample_nodes¶

sampled nodes

Type: list

dynamicgem.evaluation.evaluate_link_prediction.is_undirected¶

Flag to denote if the graph is directed.

Type: bool

dynamicgem.evaluation.evaluate_link_prediction.sampling_scheme¶

Sampling scheme to be used.

type

str

Returns:: ndarray: MAP, precision curve

dynamicgem.evaluation.evaluate_link_prediction.evaluateDynamicLinkPrediction_TIMERS(graph, embedding, t, rounds, n_sample_nodes=None, no_python=False, is_undirected=True, sampling_scheme='u_rand')[source]¶

Function to evaluate Dynamic Link Prediction for TIMERS

dynamicgem.evaluation.evaluate_link_prediction.graph

Networkx Graph Object

Type: Object

dynamicgem.evaluation.evaluate_link_prediction.embedding

Algorithm for learning graph embedding

Type: object

dynamicgem.evaluation.evaluate_link_prediction.t¶

sequence of the graph

Type: int

dynamicgem.evaluation.evaluate_link_prediction.n_sample_nodes

sampled nodes

Type: list

dynamicgem.evaluation.evaluate_link_prediction.is_undirected

Flag to denote if the graph is directed.

Type: bool

dynamicgem.evaluation.evaluate_link_prediction.sampling_scheme

Sampling scheme to be used.

type

str

Returns:: ndarray: MAP, precision curve

dynamicgem.evaluation.evaluate_link_prediction.evaluateDynamic_changed_LinkPrediction(graph, embedding, rounds, edges_add, edges_rm, n_sample_nodes=None, no_python=False, is_undirected=True, sampling_scheme='u_rand')[source]¶

Function to evaluate dynamic changed link prediction

dynamicgem.evaluation.evaluate_link_prediction.graph

Networkx Graph Object

Type: Object

dynamicgem.evaluation.evaluate_link_prediction.embedding

Algorithm for learning graph embedding.

Type: object

dynamicgem.evaluation.evaluate_link_prediction.edges_add¶

list of edges to be added.

Type: list

dynamicgem.evaluation.evaluate_link_prediction.edges_rm¶

list of edges to be removed.

Type: list

dynamicgem.evaluation.evaluate_link_prediction.n_sampled_nodes¶

List of sampled nodes.

Type: int

dynamicgem.evaluation.evaluate_link_prediction.train_ratio_init¶

sample to be used for training and testing.

Type: float

dynamicgem.evaluation.evaluate_link_prediction.rounds¶

Number of times to run the experiment

Type: int

dynamicgem.evaluation.evaluate_link_prediction.m_summ¶

summary to be used to save the result.

Type: str

dynamicgem.evaluation.evaluate_link_prediction.is_undirected

Flag to denote if the graph is directed.

Type: bool

dynamicgem.evaluation.evaluate_link_prediction.sampling_scheme

sampling scheme for selecting the nodes.

type

str

Returns:: ndarray: Mean Average precision

dynamicgem.evaluation.evaluate_link_prediction.evaluateDynamic_changed_LinkPrediction_v2(graph, embedding, rounds, edges_add, edges_rm, n_sample_nodes=None, no_python=False, is_undirected=True, sampling_scheme='u_rand')[source]¶

Function to evaluate dynamic changed link prediction

dynamicgem.evaluation.evaluate_link_prediction.graph

Networkx Graph Object

Type: Object

dynamicgem.evaluation.evaluate_link_prediction.embedding

Algorithm for learning graph embedding.

Type: object

dynamicgem.evaluation.evaluate_link_prediction.edges_add

list of edges to be added.

Type: list

dynamicgem.evaluation.evaluate_link_prediction.edges_rm

list of edges to be removed.

Type: list

dynamicgem.evaluation.evaluate_link_prediction.n_sampled_nodes

List of sampled nodes.

Type: int

dynamicgem.evaluation.evaluate_link_prediction.train_ratio_init

sample to be used for training and testing.

Type: float

dynamicgem.evaluation.evaluate_link_prediction.rounds

Number of times to run the experiment

Type: int

dynamicgem.evaluation.evaluate_link_prediction.m_summ

summary to be used to save the result.

Type: str

dynamicgem.evaluation.evaluate_link_prediction.is_undirected

Flag to denote if the graph is directed.

Type: bool

dynamicgem.evaluation.evaluate_link_prediction.sampling_scheme

sampling scheme for selecting the nodes.

type

str

Returns:: ndarray: Mean Average precision

dynamicgem.evaluation.evaluate_link_prediction.expLP(graphs, embedding, rounds, res_pre, m_summ, n_sample_nodes=1000, train_ratio_init=0.5, no_python=False, is_undirected=True, sampling_scheme='u_rand')[source]¶

Function to evaluate link prediction

dynamicgem.evaluation.evaluate_link_prediction.digraph¶

Networkx Graph Object

Type: Object

dynamicgem.evaluation.evaluate_link_prediction.graph_embedding¶

Algorithm for learning graph embedding

Type: object

dynamicgem.evaluation.evaluate_link_prediction.X_stat¶

Embedding values of the graph.

Type: ndarray

dynamicgem.evaluation.evaluate_link_prediction.n_sampled_nodes

List of sampled nodes.

Type: int

dynamicgem.evaluation.evaluate_link_prediction.train_ratio_init

sample to be used for training and testing.

Type: float

dynamicgem.evaluation.evaluate_link_prediction.rounds

Number of times to run the experiment

Type: int

dynamicgem.evaluation.evaluate_link_prediction.res_pre¶

prefix to be used to store the result.

Type: str

dynamicgem.evaluation.evaluate_link_prediction.m_summ

summary to be used to save the result.

Type: str

dynamicgem.evaluation.evaluate_link_prediction.file_suffix¶

Suffix for file name.

Type: str

dynamicgem.evaluation.evaluate_link_prediction.is_undirected

Flag to denote if the graph is directed.

Type: bool

dynamicgem.evaluation.evaluate_link_prediction.sampling_scheme

sampling scheme for selecting the nodes.

type

str

Returns:: ndarray: Mean Average precision

dynamicgem.evaluation.evaluate_link_prediction.exp_changedLP(graphs, embedding, rounds, res_pre, m_summ, n_sample_nodes=1000, train_ratio_init=0.5, no_python=False, is_undirected=True, sampling_scheme='u_rand')[source]¶

Function to evaluate only changed link prediction

dynamicgem.evaluation.evaluate_link_prediction.digraph

Networkx Graph Object

Type: Object

dynamicgem.evaluation.evaluate_link_prediction.graph_embedding

Algorithm for learning graph embedding

Type: object

dynamicgem.evaluation.evaluate_link_prediction.X_stat

Embedding values of the graph.

Type: ndarray

dynamicgem.evaluation.evaluate_link_prediction.n_sampled_nodes

List of sampled nodes.

Type: int

dynamicgem.evaluation.evaluate_link_prediction.train_ratio_init

sample to be used for training and testing.

Type: float

dynamicgem.evaluation.evaluate_link_prediction.rounds

Number of times to run the experiment

Type: int

dynamicgem.evaluation.evaluate_link_prediction.res_pre

prefix to be used to store the result.

Type: str

dynamicgem.evaluation.evaluate_link_prediction.m_summ

summary to be used to save the result.

Type: str

dynamicgem.evaluation.evaluate_link_prediction.file_suffix

Suffix for file name.

Type: str

dynamicgem.evaluation.evaluate_link_prediction.is_undirected

Flag to denote if the graph is directed.

Type: bool

dynamicgem.evaluation.evaluate_link_prediction.sampling_scheme

sampling scheme for selecting the nodes.

type

str

Returns:: ndarray: Mean Average precision

dynamicgem.evaluation.evaluate_link_prediction.expstaticLP(dynamic_sbm_series, graphs, embedding, rounds, res_pre, m_summ, n_sample_nodes=1000, train_ratio_init=0.5, no_python=False, is_undirected=True, sampling_scheme='u_rand')[source]¶

Function to evaluate statically changed link prediction

dynamicgem.evaluation.evaluate_link_prediction.dynamic_sbm_series¶

list of Networkx Graph Object

Type: list

dynamicgem.evaluation.evaluate_link_prediction.gaphs¶

Networkx graphs

Type: object

dynamicgem.evaluation.evaluate_link_prediction.embedding

Algorithm for learning graph embedding

Type: object

dynamicgem.evaluation.evaluate_link_prediction.n_sampled_nodes

List of sampled nodes.

Type: int

dynamicgem.evaluation.evaluate_link_prediction.train_ratio_init

sample to be used for training and testing.

Type: float

dynamicgem.evaluation.evaluate_link_prediction.rounds

Number of times to run the experiment

Type: int

dynamicgem.evaluation.evaluate_link_prediction.res_pre

prefix to be used to store the result.

Type: str

dynamicgem.evaluation.evaluate_link_prediction.m_summ

summary to be used to save the result.

Type: str

dynamicgem.evaluation.evaluate_link_prediction.file_suffix

Suffix for file name.

Type: str

dynamicgem.evaluation.evaluate_link_prediction.is_undirected

Flag to denote if the graph is directed.

Type: bool

dynamicgem.evaluation.evaluate_link_prediction.sampling_scheme

sampling scheme for selecting the nodes.

type

str

Returns:: ndarray: Mean Average precision

dynamicgem.evaluation.evaluate_link_prediction.expstaticLP_TIMERS(dynamic_sbm_series, graphs, embedding, rounds, res_pre, m_summ, n_sample_nodes=1000, train_ratio_init=0.5, no_python=False, is_undirected=True, sampling_scheme='u_rand')[source]¶

Function to evaluate statically changed link prediction for TIMERS

dynamicgem.evaluation.evaluate_link_prediction.dynamic_sbm_series

list of Networkx Graph Object

Type: list

dynamicgem.evaluation.evaluate_link_prediction.gaphs

Networkx graphs

Type: object

dynamicgem.evaluation.evaluate_link_prediction.embedding

Algorithm for learning graph embedding

Type: object

dynamicgem.evaluation.evaluate_link_prediction.n_sampled_nodes

List of sampled nodes.

Type: int

dynamicgem.evaluation.evaluate_link_prediction.train_ratio_init

sample to be used for training and testing.

Type: float

dynamicgem.evaluation.evaluate_link_prediction.rounds

Number of times to run the experiment

Type: int

dynamicgem.evaluation.evaluate_link_prediction.res_pre

prefix to be used to store the result.

Type: str

dynamicgem.evaluation.evaluate_link_prediction.m_summ

summary to be used to save the result.

Type: str

dynamicgem.evaluation.evaluate_link_prediction.file_suffix

Suffix for file name.

Type: str

dynamicgem.evaluation.evaluate_link_prediction.is_undirected

Flag to denote if the graph is directed.

Type: bool

dynamicgem.evaluation.evaluate_link_prediction.sampling_scheme

sampling scheme for selecting the nodes.

type

str

Returns:: ndarray: Mean Average precision

dynamicgem.evaluation.evaluate_link_prediction.expstaticLP_TRIAD(dynamic_sbm_series, graphs, embedding, rounds, res_pre, m_summ, n_sample_nodes=1000, train_ratio_init=0.5, no_python=False, is_undirected=True, sampling_scheme='u_rand')[source]¶

Function to evaluate statically changed link prediction for dynamic Triad

dynamicgem.evaluation.evaluate_link_prediction.dynamic_sbm_series

list of Networkx Graph Object

Type: list

dynamicgem.evaluation.evaluate_link_prediction.gaphs

Networkx graphs

Type: object

dynamicgem.evaluation.evaluate_link_prediction.embedding

Algorithm for learning graph embedding

Type: object

dynamicgem.evaluation.evaluate_link_prediction.n_sampled_nodes

List of sampled nodes.

Type: int

dynamicgem.evaluation.evaluate_link_prediction.train_ratio_init

sample to be used for training and testing.

Type: float

dynamicgem.evaluation.evaluate_link_prediction.rounds

Number of times to run the experiment

Type: int

dynamicgem.evaluation.evaluate_link_prediction.res_pre

prefix to be used to store the result.

Type: str

dynamicgem.evaluation.evaluate_link_prediction.m_summ

summary to be used to save the result.

Type: str

dynamicgem.evaluation.evaluate_link_prediction.file_suffix

Suffix for file name.

Type: str

dynamicgem.evaluation.evaluate_link_prediction.is_undirected

Flag to denote if the graph is directed.

Type: bool

dynamicgem.evaluation.evaluate_link_prediction.sampling_scheme

sampling scheme for selecting the nodes.

type

str

Returns:: ndarray: Mean Average precision

dynamicgem.evaluation.evaluate_link_prediction.expstatic_changedLP(dynamic_sbm_series, graphs, embedding, rounds, res_pre, m_summ, n_sample_nodes=1000, train_ratio_init=0.5, no_python=False, is_undirected=True, sampling_scheme='u_rand')[source]¶

Function to evaluate statically changed link prediction

dynamicgem.evaluation.evaluate_link_prediction.dynamic_sbm_series

list of Networkx Graph Object

Type: list

dynamicgem.evaluation.evaluate_link_prediction.gaphs

Networkx graphs

Type: object

dynamicgem.evaluation.evaluate_link_prediction.embedding

Algorithm for learning graph embedding

Type: object

dynamicgem.evaluation.evaluate_link_prediction.n_sampled_nodes

List of sampled nodes.

Type: int

dynamicgem.evaluation.evaluate_link_prediction.train_ratio_init

sample to be used for training and testing.

Type: float

dynamicgem.evaluation.evaluate_link_prediction.rounds

Number of times to run the experiment

Type: int

dynamicgem.evaluation.evaluate_link_prediction.res_pre

prefix to be used to store the result.

Type: str

dynamicgem.evaluation.evaluate_link_prediction.m_summ

summary to be used to save the result.

Type: str

dynamicgem.evaluation.evaluate_link_prediction.file_suffix

Suffix for file name.

Type: str

dynamicgem.evaluation.evaluate_link_prediction.is_undirected

Flag to denote if the graph is directed.

Type: bool

dynamicgem.evaluation.evaluate_link_prediction.sampling_scheme

sampling scheme for selecting the nodes.

type

str

Returns:: ndarray: Mean Average precision

dynamicgem.evaluation.evaluate_link_prediction.getchangedlinks(G, Gnew)[source]¶: Functionto get all the changed links

Metrics¶

dynamicgem.evaluation.metrics.checkedges(edge_list, e)[source]¶

Function to check if the given edgelist matches.

dynamicgem.evaluation.metrics.edge_list¶

List of predicted edges.

Type: list

dynamicgem.evaluation.metrics.e¶

Original edge list

type

list

Returns:: bool: Boolean result to denoe if all the edges matches.

dynamicgem.evaluation.metrics.computeMAP(predicted_edge_list, true_digraph, max_k=-1)[source]¶

Function to calculate Mean Average Precision

dynamicgem.evaluation.metrics.predicted_edge_list¶

List of predicted edges.

Type: list

dynamicgem.evaluation.metrics.true_digraph¶

original graph

Type: object

dynamicgem.evaluation.metrics.max_k¶

precision@k

type

int

Returns:: Float: Mean Average Precision score

dynamicgem.evaluation.metrics.computeMAP_changed(predicted_edge_list, true_digraph, node_dict, edges_rm, max_k=-1)[source]¶

Function to calculate MAP of the change graph

dynamicgem.evaluation.metrics.predicted_edge_list

List of predicted edges.

Type: list

dynamicgem.evaluation.metrics.true_digraph

original graph

Type: object

dynamicgem.evaluation.metrics.node_dict¶

Dictionary for the nodes.

Type: dict

dynamicgem.evaluation.metrics.node_edges_rm¶

list of edges removed from the original graph.

Type: list

dynamicgem.evaluation.metrics.max_k

precision@k

type

int

Returns:: Float: Mean Average Precision score

dynamicgem.evaluation.metrics.computePrecisionCurve(predicted_edge_list, true_digraph, max_k=-1)[source]¶

Function to calculate the precision curve

dynamicgem.evaluation.metrics.predicted_edge_list

List of predicted edges.

Type: list

dynamicgem.evaluation.metrics.true_digraph

original graph

Type: object

dynamicgem.evaluation.metrics.max_k

precision@k

type

int

Returns:: ndarray: precision_scores, delta_factors

dynamicgem.evaluation.metrics.computePrecisionCurve_changed(predicted_edge_list, true_digraph, node_edges_rm, max_k=-1)[source]¶

Function to calculate Preicison curve of changed graph

dynamicgem.evaluation.metrics.predicted_edge_list

List of predicted edges.

Type: list

dynamicgem.evaluation.metrics.true_digraph

original graph

Type: object

dynamicgem.evaluation.metrics.node_edges_rm

list of edges removed from the original graph.

Type: list

dynamicgem.evaluation.metrics.max_k

precision@k

type

int

Returns:: Float: Mean Average Precision score

dynamicgem.evaluation.metrics.getEmbeddingShift(X1, X2, S1, S2)[source]¶: Function to get the shift in embedding

dynamicgem.evaluation.metrics.getMetricsHeader()[source]¶: Function to get the header for storing the result

dynamicgem.evaluation.metrics.getNodeAnomaly(X_dyn)[source]¶: Function to get the node anomaly

dynamicgem.evaluation.metrics.getPrecisionReport(prec_curv, edge_num)[source]¶: Function to get the report summary for precision

dynamicgem.evaluation.metrics.getStabilityDev(X1, X2, S1, S2)[source]¶: Function to get the deviation froms stability

Embedding Visualization¶

dynamicgem.evaluation.visualize_embedding.expVis(X, res_pre, m_summ, node_labels=None, di_graph=None)[source]¶: Function to perform visualixe the experiments of dynamic graph

dynamicgem.evaluation.visualize_embedding.plot_embedding2D(node_pos, node_colors=None, di_graph=None)[source]¶: Function to plot the embedding in two dimension using TSNE to reduce the dimension

dynamicgem.evaluation.visualize_embedding.plot_single_step(node_pos, graph_info, dyn_changed_node)[source]¶: Function to plot a single step

dynamicgem.evaluation.visualize_embedding.plot_static_sbm_embedding(nodes_pos_list, dynamic_sbm_series)[source]¶: Function to plot the static sbm embedding

Experiments¶

Config¶

Experiments¶

dynamicgem.experiments.exp.call_exps(params, data_set, n_graphs)[source]¶

Function to run the experiments

dynamicgem.experiments.exp.n_graphs¶

Total number of graphs in a sequence.

Type: int

dynamicgem.experiments.exp.data_set¶

Name of the dataset to be used for the experiment

Type: str

dynamicgem.experiments.exp.params¶

Dictionary of parameters necessary for running the experiment

Type: dict

dynamicgem.experiments.exp.call_plot_hyp(data_set, params)[source]¶

Function to plot the result of hyperparameter search

dynamicgem.experiments.exp.data_set

Name of the dataset to be used for the experiment

Type: str

dynamicgem.experiments.exp.params

Dictionary of parameters necessary for running the experiment

Type: dict

dynamicgem.experiments.exp.call_plot_hyp_all(data_sets, params)[source]¶

Function to plot the the result of all the hyper-parameters

dynamicgem.experiments.exp.data_set

Name of the dataset to be used for the experiment

Type: str

dynamicgem.experiments.exp.params

Dictionary of parameters necessary for running the experiment

Type: dict

dynamicgem.experiments.exp.choose_best_hyp(data_set, graphs, params)[source]¶

Function to get the best hyperparameter using a grid search

dynamicgem.experiments.exp.data_set

Name of the dataset to be used for the experiment

Type: str

dynamicgem.experiments.exp.graphs¶

Networkx Graph Object

Type: Object

dynamicgem.experiments.exp.params

Dictionary of parameters necessary for running the experiment

Type: dict

dynamicgem.experiments.exp.get_max(val, val_max, idx, idx_max)[source]¶: Function to get the maximum value.

dynamicgem.experiments.exp.learn_emb(MethObj, graphs, params, res_pre, m_summ)[source]¶

Function to learn embedding

dynamicgem.experiments.exp.MethObj¶

Object of the algorithm class

Type: obj

dynamicgem.experiments.exp.graphs

Networkx Graph Object

Type: Object

dynamicgem.experiments.exp.params

Dictionary of parameters necessary for running the experiment

Type: dict

dynamicgem.experiments.exp.res_pre¶

Prefix of the filename for saving the result.

Type: str

dynamicgem.experiments.exp.m_summ¶

summary added to the filename of the result.

type

str

Returns:: ndarray: Learned embedding

dynamicgem.experiments.exp.run_exps(MethObj, meth, dim, graphs, data_set, params)[source]¶

Function to run the experiment

dynamicgem.experiments.exp.MethObj

Object of the algorithm class

Type: obj

dynamicgem.experiments.exp.meth¶

Name of the method

Type: str

dynamicgem.experiments.exp.dim¶

Dimension of the embedding

Type: int

dynamicgem.experiments.exp.graphs

Networkx Graph Object

Type: Object

dynamicgem.experiments.exp.data_set

Name of the dataset to be used for the experiment

Type: str

dynamicgem.experiments.exp.params

Dictionary of parameters necessary for running the experiment

type

dict

Returns:: ndarray: Learned embedding

Graph Generation¶

Dynamic Stochastic Block Model Graph¶

dynamicgem.graph_generation.dynamic_SBM_graph.diminish_community(sbm_graph, community_id, nodes_to_purturb, criteria, criteria_r)[source]¶

Function to diminsh the SBM community

dynamicgem.graph_generation.dynamic_SBM_graph.sbm_graph¶

Networkx Graph Object

Type: Object

dynamicgem.graph_generation.dynamic_SBM_graph.community_id¶

Community to diminish

Type: int

dynamicgem.graph_generation.dynamic_SBM_graph.criteria¶

Criteria used to diminish the community

Type: str

dynamicgem.graph_generation.dynamic_SBM_graph.criteria_r¶

Used to sort the nodes in reverse once order based on criteria

Type: bool

dynamicgem.graph_generation.dynamic_SBM_graph.nodes_to_purturb¶

Number of nodes to perturb

Type: int

dynamicgem.graph_generation.dynamic_SBM_graph.diminish_community_v2(sbm_graph, community_id, nodes_to_purturb, chngnodes)[source]¶

Function to diminsh the SBM community

dynamicgem.graph_generation.dynamic_SBM_graph.sbm_graph

Networkx Graph Object

Type: Object

dynamicgem.graph_generation.dynamic_SBM_graph.community_id

Community to diminish

Type: int

dynamicgem.graph_generation.dynamic_SBM_graph.nodes_to_purturb

Number of nodes to perturb

Type: int

dynamicgem.graph_generation.dynamic_SBM_graph.chngnodes¶

List of nodes that is perturbed

Type: list

dynamicgem.graph_generation.dynamic_SBM_graph.drawGraph(node_num, community_num)[source]¶: Function to draw the graphs

dynamicgem.graph_generation.dynamic_SBM_graph.dyn_node_chng(sbm_graph, node_id)[source]¶

Function to dynamically change the nodes

dynamicgem.graph_generation.dynamic_SBM_graph.sbm_graph

Networkx Graph Object

Type: Object

dynamicgem.graph_generation.dynamic_SBM_graph.node_id¶

Id of the node to resample

Type: int

dynamicgem.graph_generation.dynamic_SBM_graph.dyn_node_chng_v2(sbm_graph, node_id)[source]¶

Function to dynamically change the nodes

dynamicgem.graph_generation.dynamic_SBM_graph.sbm_graph

Networkx Graph Object

Type: Object

dynamicgem.graph_generation.dynamic_SBM_graph.node_id

Id of the node to resample

Type: int

dynamicgem.graph_generation.dynamic_SBM_graph.get_community_diminish_series(node_num, community_num, length, community_id, nodes_to_purturb, criteria, criteria_r)[source]¶

Function to get diminshing community series

dynamicgem.graph_generation.dynamic_SBM_graph.node_num¶

Total number of nodes

Type: int

dynamicgem.graph_generation.dynamic_SBM_graph.community_num¶

Total number of community

Type: int

dynamicgem.graph_generation.dynamic_SBM_graph.nodes_to_purturb

Number of nodes to perturb

Type: int

dynamicgem.graph_generation.dynamic_SBM_graph.length¶

Length of the graph sequence

Type: int

dynamicgem.graph_generation.dynamic_SBM_graph.community_id

Community to diminish

Type: int

dynamicgem.graph_generation.dynamic_SBM_graph.criteria

Criteria used to diminish the community

Type: str

dynamicgem.graph_generation.dynamic_SBM_graph.criteria_r

Used to sort the nodes in reverse once order based on criteria

Type: bool

dynamicgem.graph_generation.dynamic_SBM_graph.get_community_diminish_series_v2(node_num, community_num, length, community_id, nodes_to_purturb)[source]¶

Function to get diminishing community series

dynamicgem.graph_generation.dynamic_SBM_graph.node_num

Total number of nodes

Type: int

dynamicgem.graph_generation.dynamic_SBM_graph.community_num

Total number of community

Type: int

dynamicgem.graph_generation.dynamic_SBM_graph.nodes_to_purturb

Number of nodes to perturb

Type: int

dynamicgem.graph_generation.dynamic_SBM_graph.length

Length of the graph sequence

Type: int

dynamicgem.graph_generation.dynamic_SBM_graph.community_id

Community to diminish

Type: int

dynamicgem.graph_generation.dynamic_SBM_graph.get_random_perturbation_series(node_num, community_num, length, nodes_to_purturb)[source]¶

Function to get random perturbation

dynamicgem.graph_generation.dynamic_SBM_graph.node_num

Total number of nodes

Type: int

dynamicgem.graph_generation.dynamic_SBM_graph.community_num

Total number of community

Type: int

dynamicgem.graph_generation.dynamic_SBM_graph.nodes_to_purturb

Number of nodes to perturb

Type: int

dynamicgem.graph_generation.dynamic_SBM_graph.length

Length of the graph sequence

Type: int

dynamicgem.graph_generation.dynamic_SBM_graph.random_node_perturbation(sbm_graph, nodes_to_purturb)[source]¶

Function to randomly perturb the nodes

dynamicgem.graph_generation.dynamic_SBM_graph.sbm_graph

Networkx Graph Object

Type: Object

dynamicgem.graph_generation.dynamic_SBM_graph.nodes_to_purturb

Number of nodes to perturb

Type: int