5. Graph algorithms

The Analyst class can be used to execute algorithms on a given graph.

class pypgx.api.Analyst(session, java_analyst)

The Analyst gives access to all built-in algorithms of PGX.

Unlike some of the other classes inside this package, the Analyst is not stateless. It creates session-bound transient data to hold the result of algorithms and keeps track of them.

adamic_adar_counting(graph, aa='adamic_adar')

Adamic-adar counting compares the amount of neighbors shared between vertices, this measure can be used with communities.

Parameters

  • graph – Input graph

  • aa – Edge property holding the Adamic-Adar index for each edge in the graph. Can be a string or an EdgeProperty object.

Returns

Edge property holding the computed scores

all_reachable_vertices_edges(graph, src, dst, k, filter=None)

Find all the vertices and edges on a path between the src and target of length smaller or equal to k.

Parameters

  • graph – Input graph

  • src – The source vertex

  • dst – The destination vertex

  • k – The dimension of the distances property; i.e. number of high-degree vertices.

  • filter – The filter to be used on edges when searching for a path

Returns

The vertices on the path, the edges on the path and a map containing the distances from the source vertex for each vertex on the path

approximate_vertex_betweenness_centrality(graph, seeds, bc='approx_betweenness')
Parameters

  • graph – Input graph

  • seeds – The (unique) chosen nodes to be used to compute the approximated betweenness centrality coefficients

  • bc – Vertex property holding the betweenness centrality value for each vertex

Returns

Vertex property holding the computed scores

bipartite_check(graph, is_left='is_left')

Verify whether a graph is bipartite.

Parameters

  • graph – Input graph

  • is_left – (out-argument) vertex property holding the side of each vertex in a bipartite graph (true for left, false for right).

Returns

vertex property holding the side of each vertex in a bipartite graph (true for left, false for right).

center(graph, center=None)

Periphery/center gives an overview of the extreme distances and the corresponding vertices in a graph.

The center is comprised by the set of vertices with eccentricity equal to the radius of the graph.

Parameters

  • graph – Input graph

  • center – (Out argument) vertex set holding the vertices from the periphery or center of the graph

close()

Destroy without waiting for completion.

closeness_centrality(graph, cc='closeness')
Parameters

  • graph – Input graph

  • cc – Vertex property holding the closeness centrality

communities_conductance_minimization(graph, max_iter=100, label='conductance_minimization')

Soman and Narang can find communities in a graph taking weighted edges into account.

Parameters

  • graph – Input graph

  • max_iter – Maximum number of iterations that will be performed

  • label – Vertex property holding the degree centrality value for each vertex in the graph. Can be a string or a VertexProperty object.

  • label – Vertex property holding the degree centrality value for each vertex in the graph. Can be a string or a VertexProperty object.

Returns

Partition holding the node collections corresponding to the communities found by the algorithm

Returns

Partition holding the node collections corresponding to the communities found by the algorithm

communities_infomap(graph, rank, weight, tau=0.15, tol=0.0001, max_iter=100, label='infomap')

Infomap can find high quality communities in a graph.

Parameters

  • graph – Input graph

  • rank – Vertex property holding the normalized PageRank value for each vertex

  • weight – Ridge property holding the weight of each edge in the graph

  • tau – Damping factor

  • tol – Maximum tolerated error value. The algorithm will stop once the sum of the error values of all vertices becomes smaller than this value.

  • max_iter – Maximum iteration number

  • label – Vertex property holding the degree centrality value for each vertex in the graph. Can be a string or a VertexProperty object.

Returns

Partition holding the node collections corresponding to the communities found by the algorithm

communities_label_propagation(graph, max_iter=100, label='label_propagation')

Label propagation can find communities in a graph relatively fast.

Parameters

  • graph – Input graph

  • max_iter – Maximum number of iterations that will be performed

  • label – Vertex property holding the degree centrality value for each vertex in the graph. Can be a string or a VertexProperty object

  • label – Vertex property holding the degree centrality value for each vertex in the graph. Can be a string or a VertexProperty object.

Returns

Partition holding the node collections corresponding to the communities found by the algorithm

Returns

Partition holding the node collections corresponding to the communities found by the algorithm

compute_high_degree_vertices(graph, k, high_degree_vertex_mapping=None, high_degree_vertices=None)

Compute the k vertices with the highest degrees in the graph.

Parameters

  • graph – Input graph

  • k – Number of high-degree vertices to be computed

  • high_degree_vertex_mapping – (out argument) map with the top k high-degree vertices and their indices

  • high_degree_vertices – (out argument) the high-degree vertices

Returns

a map with the top k high-degree vertices and their indices and a vertex set containing the same vertices

conductance(graph, partition, partition_idx)

Conductance assesses the quality of a partition in a graph.

Parameters

  • graph – Input graph

  • partition – Partition of the graph with the corresponding node collections

  • partition_idx – Number of the component to be used for computing its conductance

count_triangles(graph, sort_vertices_by_degree)

Triangle counting gives an overview of the amount of connections between vertices in neighborhoods.

Parameters

  • graph – Input graph

  • sort_vertices_by_degree – Boolean flag for sorting the nodes by their degree as preprocessing step

Returns

The total number of triangles found

create_distance_index(graph, high_degree_vertex_mapping, high_degree_vertices, index=None)

Compute an index with distances to each high-degree vertex

Parameters

  • graph – Input graph

  • high_degree_vertex_mapping – a map with the top k high-degree vertices and their indices and a vertex

  • high_degree_vertices – the high-degree vertices

  • index – (out-argument) the index containing the distances to each high-degree vertex for all vertices

Returns

the index containing the distances to each high-degree vertex for all vertices

deepwalk_builder(min_word_frequency=1, batch_size=128, num_epochs=2, layer_size=200, learning_rate=0.025, min_learning_rate=0.0001, window_size=5, walk_length=5, walks_per_vertex=4, sample_rate=1e-05, negative_sample=10, validation_fraction=0.05, seed=None)

Build a DeepWalk model and return it.

Parameters

  • min_word_frequency – Minimum word frequency to consider before pruning

  • batch_size – Batch size for training the model

  • num_epochs – Number of epochs to train the model

  • layer_size – Number of dimensions for the output vectors

  • learning_rate – Initial learning rate

  • min_learning_rate – Minimum learning rate

  • window_size – Window size to consider while training the model

  • walk_length – Length of the walks

  • walks_per_vertex – Number of walks to consider per vertex

  • sample_rate – Sample rate

  • negative_sample – Number of negative samples

  • validation_fraction – Fraction of training data on which to compute final loss

  • seed – Random seed for training the model

Returns

Built DeepWalk model

degree_centrality(graph, dc='degree')

Measure the centrality of the vertices based on its degree.

This lets you see how a vertex influences its neighborhood.

Parameters

  • graph – Input graph

  • dc – Vertex property holding the degree centrality value for each vertex in the graph. Can be a string or a VertexProperty object.

Returns

Vertex property holding the computed scores

destroy()

Destroy with waiting for completion.

diameter(graph, eccentricity='eccentricity')

Diameter/radius gives an overview of the distances in a graph.

Parameters

  • graph – Input graph

  • eccentricity – (Out argument) vertex property holding the eccentricity value for each vertex

Returns

Pair holding the diameter of the graph and a node property with eccentricity value for each node

eigenvector_centrality(graph, tol=0.001, max_iter=100, l2_norm=False, in_edges=False, ec='eigenvector')

Eigenvector centrality gets the centrality of the vertices in an intrincated way using neighbors, allowing to find well-connected vertices.

Parameters

  • graph – Input graph

  • tol – Maximum tolerated error value. The algorithm will stop once the sum of the error values of all vertices becomes smaller than this value.

  • max_iter – Maximum iteration number

  • l2_norm – Boolean flag to determine whether the algorithm will use the l2 norm (Euclidean norm) or the l1 norm (absolute value) to normalize the centrality scores

  • in_edges – Boolean flag to determine whether the algorithm will use the incoming or the outgoing edges in the graph for the computations

  • ec – Vertex property holding the resulting score for each vertex

Returns

Vertex property holding the computed scores

enumerate_simple_paths(graph, src, dst, k, vertices_on_path, edges_on_path, dist)

Enumerate simple paths between the source and destination vertex.

Parameters

  • graph – Input graph

  • src – The source vertex

  • dst – The destination vertex

  • k – maximum number of iterations

  • vertices_on_path – VertexSet containing all vertices to be considered while enumerating paths

  • edges_on_path – EdgeSet containing all edges to be consider while enumerating paths

  • dist – map containing the hop-distance from the source vertex to each vertex that is to be considered while enumerating the paths

Returns

Triple containing containing the path lengths, a vertex-sequence containing the vertices on the paths and edge-sequence containing the edges on the paths

fattest_path(graph, root, capacity, distance='fattest_path_distance', parent='fattest_path_parent', parent_edge='fattest_path_parent_edge')

Fattest path is a fast algorithm for finding a shortest path adding constraints for flowing related matters.

Parameters

  • graph – Input graph

  • root – Fattest path is a fast algorithm for finding a shortest path adding constraints for flowing related matters

  • capacity – Edge property holding the capacity of each edge in the graph

  • distance – Vertex property holding the capacity value of the fattest path up to the current vertex

  • parent – Vertex property holding the parent vertex of the each vertex in the fattest path

  • parent_edge – Vertex property holding the edge ID linking the current vertex in the path with the previous vertex in the path

Returns

AllPaths object holding the information of the possible fattest paths from the source node

filtered_bfs(graph, root, navigator, init_with_inf=True, max_depth=2147483647, distance='distance', parent='parent')

Breadth-first search with an option to filter edges during the traversal of the graph.

Parameters

  • graph – Input graph

  • root – The source vertex from the graph for the path.

  • navigator – Navigator expression to be evaluated on the vertices during the graph traversal

  • init_with_inf – Boolean flag to set the initial distance values of the vertices. If set to true, it will initialize the distances as INF, and -1 otherwise.

  • max_depth – Maximum depth limit for the BFS traversal

  • distance – Vertex property holding the hop distance for each reachable vertex in the graph

  • parent – Vertex property holding the parent vertex of the each reachable vertex in the path

Returns

Distance and parent vertex properties

filtered_dfs(graph, root, navigator, init_with_inf=True, max_depth=2147483647, distance='distance', parent='parent')

Depth-first search with an option to filter edges during the traversal of the graph.

Parameters

  • graph – Input graph

  • root – The source vertex from the graph for the path

  • navigator – Navigator expression to be evaluated on the vertices during the graph traversal

  • init_with_inf – Boolean flag to set the initial distance values of the vertices. If set to true, it will initialize the distances as INF, and -1 otherwise.

  • max_depth – Maximum search depth

  • distance – Vertex property holding the hop distance for each reachable vertex in the graph

  • parent – Vertex property holding the parent vertex of the each reachable vertex in the path

Returns

Distance and parent vertex properties

find_cycle(graph, src=None, vertex_seq=None, edge_seq=None)

Find cycle looks for any loop in the graph.

Parameters

  • graph – Input graph

  • src – Source vertex for the search

  • vertex_seq – (Out argument) vertex sequence holding the vertices in the cycle

  • edge_seq – (Out argument) edge sequence holding the edges in the cycle

Returns

PgxPath representing the cycle as path, if exists.

get_deepwalk_model_loader()

Return a ModelLoader that can be used for loading a DeepWalkModel.

Returns

ModelLoader

get_pg2vec_model_loader()

Return a ModelLoader that can be used for loading a Pg2vecModel.

Returns

ModelLoader

get_supervised_graphwise_model_loader()

Return a ModelLoader that can be used for loading a SupervisedGraphWiseModel.

Returns

ModelLoader

get_unsupervised_graphwise_model_loader()

Return a ModelLoader that can be used for loading a UnsupervisedGraphWiseModel.

Returns

ModelLoader

graphwise_conv_layer_config(num_sampled_neighbors=10, neighbor_weight_property_name=None, activation_fn='ReLU', weight_init_scheme='XAVIER_UNIFORM')

Build a GraphWise conv layer configuration and return it.

Parameters

  • num_sampled_neighbors – Number of neighbors to sample

  • neighbor_weight_property_name – Neighbor weight property name.

  • activation_fn – Activation function. Supported functions: RELU, LEAKY_RELU, TANH, LINEAR. If this is the last layer, this setting will be ignored and replaced by the activation function of the loss function, e.g softmax or sigmoid.

  • weight_init_scheme – Initialization scheme for the weights in the layer. Supported schemes: XAVIER, XAVIER_UNIFORM, ONES, ZEROS. Note that biases are always initialized with zeros.

Returns

Built GraphWiseConvLayerConfig

graphwise_dgi_layer_config(corruption_function=None, readout_function='MEAN', discriminator='BILINEAR')

Build a GraphWise DGI layer configuration and return it.

Parameters

  • corruption_function(CorruptionFunction) – Corruption Function to use

  • readout_function(str) – Neighbor weight property name. Supported functions: MEAN

  • discriminator(str) – discriminator function. Supported functions: BILINEAR

Returns

GraphWiseDgiLayerConfig object

graphwise_pred_layer_config(hidden_dim=None, activation_fn='ReLU', weight_init_scheme='XAVIER_UNIFORM')

Build a GraphWise prediction layer configuration and return it.

Parameters

  • hidden_dim – Hidden dimension. If this is the last layer, this setting will be ignored and replaced by the number of classes.

  • activation_fn – Activation function. Supported functions: RELU, LEAKY_RELU, TANH, LINEAR. If this is the last layer, this setting will be ignored and replaced by the activation function of the loss function, e.g softmax or sigmoid.

  • weight_init_scheme – Initialization scheme for the weights in the layer. Supportes schemes: XAVIER, XAVIER_UNIFORM, ONES, ZEROS. Note that biases are always initialized with zeros.

Returns

Built GraphWisePredictionLayerConfig

hits(graph, max_iter=100, auth='authorities', hubs='hubs')

Hyperlink-Induced Topic Search (HITS) assigns ranking scores to the vertices, aimed to assess the quality of information and references in linked structures.

Parameters

  • graph – Input graph

  • max_iter – Number of iterations that will be performed

  • auth – Vertex property holding the authority score for each vertex

  • hubs – Vertex property holding the hub score for each vertex

Returns

Vertex property holding the computed scores

in_degree_centrality(graph, dc='in_degree')

Measure the in-degree centrality of the vertices based on its degree.

This lets you see how a vertex influences its neighborhood.

Parameters

  • graph – Input graph

  • dc – Vertex property holding the degree centrality value for each vertex in the graph. Can be a string or a VertexProperty object.

Returns

Vertex property holding the computed scores

in_degree_distribution(graph, dist_map=None)

Calculate the in-degree distribution.

In-degree distribution gives information about the incoming flows in a graph.

Parameters

  • graph – Input graph

  • dist_map – (Out argument) map holding a histogram of the vertex degrees in the graph

Returns

Map holding a histogram of the vertex degrees in the graph

k_core(graph, min_core=0, max_core=2147483647, kcore='kcore')

k-core decomposes a graph into layers revealing subgraphs with particular properties.

Parameters

  • graph – Input graph

  • min_core – Minimum k-core value

  • max_core – Maximum k-core value

  • kcore – Vertex property holding the result value

Returns

Pair holding the maximum core found and a node property with the largest k-core value for each node.

limited_shortest_path_hop_dist(graph, src, dst, max_hops, high_degree_vertex_mapping, high_degree_vertices, index, path_vertices=None, path_edges=None)

Compute the shortest path between the source and destination vertex.

The algorithm only considers paths up to a length of k.

Parameters

  • graph – Input graph

  • src – The source vertex

  • dst – The destination vertex

  • max_hops – The maximum number of edges to follow when trying to find a path

  • high_degree_vertex_mapping – Map with the top k high-degree vertices and their indices

  • high_degree_vertices – The high-degree vertices

  • index – Index containing distances to high-degree vertices

  • path_vertices – (out-argument) will contain the vertices on the found path or will be empty if there is none

  • path_edges – (out-argument) will contain the vertices on the found path or will be empty if there is none

Returns

A tuple containing the vertices in the shortest path from src to dst and the edges on the path. Both will be empty if there is no path within maxHops steps

limited_shortest_path_hop_dist_filtered(graph, src, dst, max_hops, high_degree_vertex_mapping, high_degree_vertices, index, filter, path_vertices=None, path_edges=None)

Compute the shortest path between the source and destination vertex.

The algorithm only considers paths up to a length of k.

Parameters

  • graph – Input graph

  • src – The source vertex

  • dst – The destination vertex

  • max_hops – The maximum number of edges to follow when trying to find a path

  • high_degree_vertex_mapping – Map with the top k high-degree vertices and their indices

  • high_degree_vertices – The high-degree vertices

  • index – Index containing distances to high-degree vertices

  • filter – Filter to be evaluated on the edges when searching for a path

  • path_vertices – (out-argument) will contain the vertices on the found path or will be empty if there is none

  • path_edges – (out-argument) will contain the vertices on the found path or will be empty if there is none

Returns

A tuple containing the vertices in the shortest path from src to dst and the edges on the path. Both will be empty if there is no path within maxHops steps

load_deepwalk_model(path, key)

Load an encrypted DeepWalk model.

Parameters

  • path – Path to model

  • key – The decryption key, or null if no encryption was used

Returns

Loaded model

load_pg2vec_model(path, key)

Load an encrypted pg2vec model.

Parameters

  • path – Path to model

  • key – The decryption key, or null if no encryption was used

Returns

Loaded model

load_supervised_graphwise_model(path, key)

Load an encrypted SupervisedGraphWise model.

Parameters

  • path – Path to model

  • key – The decryption key, or null if no encryption was used

Returns

Loaded model

load_unsupervised_graphwise_model(path, key)

Load an encrypted UnsupervisedGraphWise model.

Parameters

  • path – Path to model

  • key – The decryption key, or null if no encryption was used

Returns

Loaded model

local_clustering_coefficient(graph, lcc='lcc')

LCC gives information about potential clustering options in a graph.

Parameters

  • graph – Input graph

  • lcc – Vertex property holding the lcc value for each vertex

Returns

Vertex property holding the lcc value for each vertex

louvain(graph, weight, max_iter=100, nbr_pass=1, tol=0.0001, community='community')

Louvain to detect communities in a graph

Parameters

  • graph – Input graph.

  • weight – Weights of the edges of the graph.

  • max_iter – Maximum number of iterations that will be performed during each pass.

  • nbr_pass – Number of passes that will be performed.

  • tol – maximum tolerated error value, the algorithm will stop once the graph’s total modularity gain becomes smaller than this value.

  • community – Vertex property holding the community ID assigned to each vertex

Returns

Community IDs vertex property

matrix_factorization_gradient_descent(bipartite_graph, weight, learning_rate=0.001, change_per_step=1.0, lbd=0.15, max_iter=100, vector_length=10, features='features')
Parameters

  • bipartite_graph – Input graph between 1 and 5, the result will become inaccurate.

  • learning_rate – Learning rate for the optimization process

  • change_per_step – Parameter used to modulate the learning rate during the optimization process

  • lbd – Penalization parameter to avoid over-fitting during optimization process

  • max_iter – Maximum number of iterations that will be performed

  • vector_length – Size of the feature vectors to be generated for the factorization

  • features – Vertex property holding the generated feature vectors for each vertex. This function accepts names and VertexProperty objects.

Returns

Matrix factorization model holding the feature vectors found by the algorithm

matrix_factorization_recommendations(bipartite_graph, user, vector_length, feature, estimated_rating=None)

Complement for Matrix Factorization.

The generated feature vectors will be used for making predictions in cases where the given user vertex has not been related to a particular item from the item set. Similarly to the recommendations from matrix factorization, this algorithm will perform dot products between the given user vertex and the rest of vertices in the graph, giving a score of 0 to the items that are already related to the user and to the products with other user vertices, hence returning the results of the dot products for the unrelated item vertices. The scores from those dot products can be interpreted as the predicted scores for the unrelated items given a particular user vertex.

Parameters

  • bipartite_graph – Bipartite input graph

  • user – Vertex from the left (user) side of the graph

  • vector_length – size of the feature vectors

  • feature – vertex property holding the feature vectors for each vertex

  • estimated_rating – (out argument) vertex property holding the estimated rating score for each vertex

Returns

vertex property holding the estimated rating score for each vertex

out_degree_centrality(graph, dc='out_degree')

Measure the out-degree centrality of the vertices based on its degree.

This lets you see how a vertex influences its neighborhood.

Parameters

  • graph – Input graph

  • dc – Vertex property holding the degree centrality value for each vertex in the graph. Can be a string or a VertexProperty object.

Returns

Vertex property holding the computed scores

out_degree_distribution(graph, dist_map=None)
Parameters

  • graph – Input graph

  • dist_map – (Out argument) map holding a histogram of the vertex degrees in the graph

Returns

Map holding a histogram of the vertex degrees in the graph

pagerank(graph, tol=0.001, damping=0.85, max_iter=100, norm=False, rank='pagerank')
Parameters

  • graph – Input graph

  • tol – Maximum tolerated error value. The algorithm will stop once the sum of the error values of all vertices becomes smaller than this value.

  • damping – Damping factor

  • max_iter – Maximum number of iterations that will be performed

  • norm – Determine whether the algorithm will take into account dangling vertices for the ranking scores.

  • rank – Vertex property holding the PageRank value for each vertex, or name for a new property

Returns

Vertex property holding the PageRank value for each vertex

pagerank_approximate(graph, tol=0.001, damping=0.85, max_iter=100, rank='approx_pagerank')
Parameters

  • graph – Input graph

  • tol – Maximum tolerated error value. The algorithm will stop once the sum of the error values of all vertices becomes smaller than this value.

  • damping – Damping factor

  • max_iter – Maximum number of iterations that will be performed

  • rank – Vertex property holding the PageRank value for each vertex

Returns

Vertex property holding the PageRank value for each vertex

partition_conductance(graph, partition)

Partition conductance assesses the quality of many partitions in a graph.

Parameters

  • graph – Input graph

  • partition – Partition of the graph with the corresponding node collections

partition_modularity(graph, partition)

Modularity summarizes information about the quality of components in a graph.

Parameters

  • graph – Input graph

  • partition – Partition of the graph with the corresponding node collections

Returns

Scalar (double) to store the conductance value of the given cut

periphery(graph, periphery=None)

Periphery/center gives an overview of the extreme distances and the corresponding vertices in a graph.

Parameters

  • graph – Input graph

  • periphery – (Out argument) vertex set holding the vertices from the periphery or center of the graph

Returns

Vertex set holding the vertices from the periphery or center of the graph

personalized_pagerank(graph, v, tol=0.001, damping=0.85, max_iter=100, norm=False, rank='personalized_pagerank')

Personalized PageRank for a vertex of interest.

Compares and spots out important vertices in a graph.

Parameters

  • graph – Input graph

  • v – The chosen vertex from the graph for personalization

  • tol – Maximum tolerated error value. The algorithm will stop once the sum of the error values of all vertices becomes smaller than this value.

  • damping – Damping factor

  • max_iter – Maximum number of iterations that will be performed

  • norm – Boolean flag to determine whether the algorithm will take into account dangling vertices for the ranking scores.

  • rank – Vertex property holding the PageRank value for each vertex

Returns

Vertex property holding the computed scores

personalized_salsa(bipartite_graph, v, tol=0.001, damping=0.85, max_iter=100, rank='personalized_salsa')

Personalized SALSA for a vertex of interest.

Assesses the quality of information and references in linked structures.

Parameters

  • bipartite_graph – Bipartite graph

  • v – The chosen vertex from the graph for personalization

  • tol – Maximum tolerated error value. The algorithm will stop once the sum of the error values of all vertices becomes smaller than this value.

  • damping – Damping factor to modulate the degree of personalization of the scores by the algorithm

  • max_iter – Maximum number of iterations that will be performed

  • rank – (Out argument) vertex property holding the normalized authority/hub ranking score for each vertex

Returns

Vertex property holding the computed scores

personalized_weighted_pagerank(graph, v, weight, tol=0.001, damping=0.85, max_iter=100, norm=False, rank='personalized_weighted_pagerank')
Parameters

  • graph – Input graph

  • v – The chosen vertex from the graph for personalization

  • weight – Edge property holding the weight of each edge in the graph

  • tol – Maximum tolerated error value. The algorithm will stop once the sum of the error values of all vertices becomes smaller than this value.

  • damping – Damping factor

  • max_iter – Maximum number of iterations that will be performed

  • norm – Boolean flag to determine whether the algorithm will take into account dangling vertices for the ranking scores

  • rank – Vertex property holding the PageRank value for each vertex

pg2vec_builder(graphlet_id_property_name, vertex_property_names, min_word_frequency=1, batch_size=128, num_epochs=5, layer_size=200, learning_rate=0.04, min_learning_rate=0.0001, window_size=4, walk_length=8, walks_per_vertex=5, graphlet_size_property_name='graphletSize-Pg2vec', use_graphlet_size=True, validation_fraction=0.05, seed=None)

Build a pg2Vec model and return it.

Parameters

  • graphlet_id_property_name – Property name of the graphlet-id in the input graph

  • vertex_property_names – Property names to consider for pg2vec model training

  • min_word_frequency – Minimum word frequency to consider before pruning

  • batch_size – Batch size for training the model

  • num_epochs – Number of epochs to train the model

  • layer_size – Number of dimensions for the output vectors

  • learning_rate – Initial learning rate

  • min_learning_rate – Minimum learning rate

  • window_size – Window size to consider while training the model

  • walk_length – Length of the walks

  • walks_per_vertex – Number of walks to consider per vertex

  • graphlet_size_property_name – Property name for graphlet size

  • use_graphlet_size – Whether to use or not the graphlet size

  • validation_fraction – Fraction of training data on which to compute final loss

  • seed – Seed

Returns

Build Pg2Vec Model

prim(graph, weight, mst='mst')

Prim reveals tree structures with shortest paths in a graph.

Parameters

  • graph – Input graph

  • weight – Edge property holding the weight of each edge in the graph

  • mst – Edge property holding the edges belonging to the minimum spanning tree of the graph

Returns

Edge property holding the edges belonging to the minimum spanning tree of the graph (i.e. all the edges with in_mst=true)

radius(graph, eccentricity='eccentricity')

Radius gives an overview of the distances in a graph. it is computed as the minimum graph eccentricity.

Parameters

  • graph – Input graph

  • eccentricity – (Out argument) vertex property holding the eccentricity value for each vertex

Returns

Pair holding the radius of the graph and a node property with eccentricity value for each node

random_walk_with_restart(graph, source, length, reset_prob, visit_count=None)

Perform a random walk over the graph.

The walk will start at the given source vertex and will randomly visit neighboring vertices in the graph, with a probability equal to the value of reset_probability of going back to the starting point. The random walk will also go back to the starting point every time it reaches a vertex with no outgoing edges. The algorithm will stop once it reaches the specified walk length.

Parameters

  • graph – Input graph

  • source – Starting point of the random walk

  • length – Length (number of steps) of the random walk

  • reset_prob – Probability value for resetting the random walk

  • visit_count – (out argument) map holding the number of visits during the random walk for each vertex in the graph

Returns

map holding the number of visits during the random walk for each vertex in the graph

reachability(graph, src, dst, max_hops, ignore_edge_direction)

Reachability is a fast way to check if two vertices are reachable from each other.

Parameters

  • graph – Input graph

  • src – Source vertex for the search

  • dst – Destination vertex for the search

  • max_hops – Maximum hop distance between the source and destination vertices

  • ignore_edge_direction – Boolean flag for ignoring the direction of the edges during the search

Returns

The number of hops between the vertices. It will return -1 if the vertices are not connected or are not reachable given the condition of the maximum hop distance allowed.

salsa(bipartite_graph, tol=0.001, max_iter=100, rank='salsa')

Stochastic Approach for Link-Structure Analysis (SALSA) computes ranking scores.

It assesses the quality of information and references in linked structures.

Parameters

  • bipartite_graph – Bipartite graph

  • tol – Maximum tolerated error value. The algorithm will stop once the sum of the error values of all vertices becomes smaller than this value.

  • max_iter – Maximum number of iterations that will be performed

  • rank – Vertex property holding the value for each vertex in the graph

Returns

Vertex property holding the computed scores

scc_kosaraju(graph, label='scc_kosaraju')

Kosaraju finds strongly connected components in a graph.

Parameters

  • graph – Input graph

  • label – Vertex property holding the degree centrality value for each vertex in the graph. Can be a string or a VertexProperty object.

Returns

Partition holding the node collections corresponding to the components found by the algorithm

scc_tarjan(graph, label='scc_tarjan')

Tarjan finds strongly connected components in a graph.

Parameters

  • graph – Input graph

  • label – Vertex property holding the degree centrality value for each vertex in the graph. Can be a string or a VertexProperty object.

Returns

Partition holding the node collections corresponding to the components found by the algorithm

shortest_path_bellman_ford(graph, src, weight, distance='bellman_ford_distance', parent='bellman_ford_parent', parent_edge='bellman_ford_parent_edge')

Bellman-Ford finds multiple shortest paths at the same time.

Parameters

  • graph – Input graph

  • src – Source node

  • distance – (Out argument) vertex property holding the distance to the source vertex for each vertex in the graph

  • weight – Edge property holding the weight of each edge in the graph

  • parent – (Out argument) vertex property holding the parent vertex of the each vertex in the shortest path

  • parent_edge – (Out argument) vertex property holding the edge ID linking the current vertex in the path with the previous vertex in the path

Returns

AllPaths holding the information of the possible shortest paths from the source node

shortest_path_bellman_ford_reversed(graph, src, weight, distance='bellman_ford_distance', parent='bellman_ford_parent', parent_edge='bellman_ford_parent_edge')

Reversed Bellman-Ford finds multiple shortest paths at the same time.

Parameters

  • graph – Input graph

  • src – Source node

  • distance – (Out argument) vertex property holding the distance to the source vertex for each vertex in the graph

  • weight – Edge property holding the weight of each edge in the graph

  • parent – (Out argument) vertex property holding the parent vertex of the each vertex in the shortest path.

  • parent_edge – (Out argument) vertex property holding the edge ID linking the current vertex in the path with the previous vertex in the path.

Returns

AllPaths holding the information of the possible shortest paths from the source node.

shortest_path_bidirectional_dijkstra(graph, src, dst, weight, parent='bidirectional_dijkstra_parent', parent_edge='bidirectional_dijkstra_parent_edge')

Bidirectional dijkstra is a fast algorithm for finding a shortest path in a graph.

Parameters

  • graph – Input graph

  • src – Source node

  • dst – Destination node

  • weight – Edge property holding the (positive) weight of each edge in the graph

  • parent – (Out argument) vertex property holding the parent vertex of the each vertex in the shortest path

  • parent_edge – (Out argument) vertex property holding the edge ID linking the current vertex in the path with the previous vertex in the path

Returns

PgxPath holding the information of the shortest path, if it exists

shortest_path_dijkstra(graph, src, dst, weight, parent='dijkstra_parent', parent_edge='dijkstra_parent_edge')
Parameters

  • graph – Input graph

  • src – Source node

  • dst – Destination node

  • weight – Edge property holding the (positive) weight of each edge in the graph

  • parent – (Out argument) vertex property holding the parent vertex of the each vertex in the shortest path

  • parent_edge – (Out argument) vertex property holding the edge ID linking the current vertex in the path with the previous vertex in the path

Returns

PgxPath holding the information of the shortest path, if it exists

shortest_path_filtered_bidirectional_dijkstra(graph, src, dst, weight, filter_expression, parent='bidirectional_dijkstra_parent', parent_edge='bidirectional_dijkstra_parent_edge')
Parameters

  • graph – Input graph

  • src – Source node

  • dst – Destination node

  • weight – Edge property holding the (positive) weight of each edge in the graph

  • parent – (Out argument) vertex property holding the parent vertex of the each vertex in the shortest path

  • parent_edge – (Out argument) vertex property holding the edge ID linking the current vertex in the path with the previous vertex in the path

  • filter_expression – graphFilter object for filtering

Returns

PgxPath holding the information of the shortest path, if it exists

shortest_path_filtered_dijkstra(graph, src, dst, weight, filter_expression, parent='dijkstra_parent', parent_edge='dijkstra_parent_edge')
Parameters

  • graph – Input graph

  • src – Source node

  • dst – Destination node

  • weight – Edge property holding the (positive) weight of each edge in the graph

  • parent – (Out argument) vertex property holding the parent vertex of the each vertex in the shortest path

  • parent_edge – (Out argument) vertex property holding the edge ID linking the current vertex in the path with the previous vertex in the path

  • filter_expression – GraphFilter object for filtering

Returns

PgxPath holding the information of the shortest path, if it exists

shortest_path_hop_distance(graph, src, distance='hop_dist_distance', parent='hop_dist_parent', parent_edge='hop_dist_edge')

Hop distance can give a relatively fast insight on the distances in a graph.

Parameters

  • graph – Input graph

  • src – Source node

  • distance – Out argument) vertex property holding the distance to the source vertex for each vertex in the graph

  • parent – (Out argument) vertex property holding the parent vertex of the each vertex in the shortest path

  • parent_edge – (Out argument) vertex property holding the edge ID linking the current vertex in the path with the previous vertex in the path

Returns

AllPaths holding the information of the possible shortest paths from the source node

shortest_path_hop_distance_reversed(graph, src, distance='hop_dist_distance', parent='hop_dist_parent', parent_edge='hop_dist_edge')

Backwards hop distance can give a relatively fast insight on the distances in a graph.

Parameters

  • graph – Input graph

  • src – Source node

  • distance – Out argument) vertex property holding the distance to the source vertex for each vertex in the graph

  • parent – (Out argument) vertex property holding the parent vertex of the each vertex in the shortest path

  • parent_edge – (Out argument) vertex property holding the edge ID linking the current vertex in the path with the previous vertex in the path

Returns

AllPaths holding the information of the possible shortest paths from the source node

supervised_graphwise_builder(vertex_target_property_name, vertex_input_property_names=[], edge_input_property_names=[], loss_fn='SOFTMAX_CROSS_ENTROPY', batch_gen='STANDARD', batch_gen_params=[], pred_layer_config=None, conv_layer_config=None, batch_size=128, num_epochs=3, learning_rate=0.01, layer_size=128, class_weights=None, seed=None)

Build a SupervisedGraphWise model and return it.

Parameters

  • vertex_target_property_name – Target property name

  • vertex_input_property_names – Vertices Input feature names

  • edge_input_property_names – Edges Input feature names

  • loss_fn – Loss function. Supported: String (‘SOFTMAX_CROSS_ENTROPY’, ‘SIGMOID_CROSS_ENTROPY’) or LossFunction object

  • batch_gen – Batch generator. Supported: ‘STANDARD’, ‘STRATIFIED_OVERSAMPLING’

  • batch_gen_params – List of parameters passed to the batch generator

  • pred_layer_config – Prediction layer configuration as list of PredLayerConfig, or default if None

  • conv_layer_config – Conv layer configuration as list of ConvLayerConfig, or default if None

  • batch_size – Batch size for training the model

  • num_epochs – Number of epochs to train the model

  • learning_rate – Learning rate

  • layer_size – Number of dimensions for the output vectors

  • class_weights – Class weights to be used in the loss function. The loss for the corresponding class will be multiplied by the factor given in this map. If null, uniform class weights will be used.

  • seed – Seed

Returns

Built SupervisedGraphWise model

topological_schedule(graph, vs, topo_sched='topo_sched')

Topological schedule gives an order of visit for the reachable vertices from the source.

Parameters

  • graph – Input graph

  • vs – Set of vertices to be used as the starting points for the scheduling order

  • topo_sched – (Out argument) vertex property holding the scheduled order of each vertex

Returns

Vertex property holding the scheduled order of each vertex.

topological_sort(graph, topo_sort='topo_sort')

Topological sort gives an order of visit for vertices in directed acyclic graphs.

Parameters

  • graph – Input graph

  • topo_sort – (Out argument) vertex property holding the topological order of each vertex

unsupervised_graphwise_builder(vertex_input_property_names=[], edge_input_property_names=[], loss_fn='SIGMOID_CROSS_ENTROPY', conv_layer_config=None, batch_size=128, num_epochs=3, learning_rate=0.001, layer_size=128, class_weights=None, seed=None, dgi_layer_config=None)

Build a UnsupervisedGraphWise model and return it.

Parameters

  • vertex_input_property_names – Vertices Input feature names

  • edge_input_property_names – Edges Input feature names

  • loss_fn – Loss function. Supported: SIGMOID_CROSS_ENTROPY

  • conv_layer_config – Conv layer configuration as list of ConvLayerConfig, or default if None

  • batch_size – Batch size for training the model

  • num_epochs – Number of epochs to train the model

  • learning_rate – Learning rate

  • layer_size – Number of dimensions for the output vectors

  • seed – Seed

  • dgi_layer_config – Dgi layer configuration as DgiLayerConfig object, or default if None

Returns

Built UnsupervisedGraphWise model

vertex_betweenness_centrality(graph, bc='betweenness')
Parameters

  • graph – Input graph

  • bc – Vertex property holding the betweenness centrality value for each vertex

Returns

Vertex property holding the computed scores

wcc(graph, label='wcc')

Identify weakly connected components.

This can be useful for clustering graph data.

Parameters

  • graph – Input graph

  • label – Vertex property holding the value for each vertex in the graph. Can be a string or a VertexProperty object.

Returns

Partition holding the node collections corresponding to the components found by the algorithm.

weighted_closeness_centrality(graph, weight, cc='weighted_closeness')

Measure the centrality of the vertices based on weighted distances, allowing to find well-connected vertices.

Parameters

  • graph – Input graph

  • weight – Edge property holding the weight of each edge in the graph

  • cc – (Out argument) vertex property holding the closeness centrality value for each vertex

Returns

Vertex property holding the computed scores

weighted_pagerank(graph, weight, tol=0.001, damping=0.85, max_iter=100, norm=False, rank='weighted_pagerank')
Parameters

  • graph – Input graph

  • weight – Edge property holding the weight of each edge in the graph

  • tol – Maximum tolerated error value. The algorithm will stop once the sum of the error values of all vertices becomes smaller than this value.

  • damping – Damping factor

  • max_iter – Maximum number of iterations that will be performed

  • rank – Vertex property holding the PageRank value for each vertex

Returns

Vertex property holding the computed the peageRank value

whom_to_follow(graph, v, top_k=100, size_circle_of_trust=500, tol=0.001, damping=0.85, max_iter=100, salsa_tol=0.001, salsa_max_iter=100, hubs=None, auth=None)

Whom-to-follow (WTF) is a recommendation algorithm.

It returns two vertex sequences: one of similar users (hubs) and a second one with users to follow (auth).

Parameters

  • graph – Input graph

  • v – The chosen vertex from the graph for personalization of the recommendations

  • top_k – The maximum number of recommendations that will be returned

  • size_circle_of_trust – The maximum size of the circle of trust

  • tol – Maximum tolerated error value. The algorithm will stop once the sum of the error values of all vertices becomes smaller than this value.

  • damping – Damping factor for the Pagerank stage

  • max_iter – Maximum number of iterations that will be performed for the Pagerank stage

  • salsa_tol – Maximum tolerated error value for the SALSA stage

  • salsa_max_iter – Maximum number of iterations that will be performed for the SALSA stage

  • hubs – (Out argument) vertex sequence holding the top rated hub vertices (similar users) for the recommendations

  • auth – (Out argument) vertex sequence holding the top rated authority vertices (users to follow) for the recommendations

Returns

Vertex properties holding hubs and auth