API Reference

Graph

class cugraph.Graph

cuGraph graph class containing basic graph creation and transformation operations.

Methods

add_adj_list(self, offset_col, index_col[, …])

Create the adjacency list representation of a Graph.

add_edge_list(self, source_col, dest_col[, …])

Create the edge list representation of a Graph.

add_transposed_adj_list(self)

Compute the transposed adjacency list from the edge list and add it to the existing graph.

clear(self)

Empty this graph.

degree(self[, vertex_subset])

Calculates and returns the degree of vertices.

delete_adj_list(self)

Delete the adjacency list.

delete_edge_list(self)

Delete the edge list.

delete_transposed_adj_list(self)

Delete the transposed adjacency list.

get_two_hop_neighbors(self)

Return a dataframe containing vertex pairs such that each pair of vertices is connected by a path of two hops in the graph.

in_degree(self[, vertex_subset])

Calculates and returns the in-degree of vertices.

number_of_edges(self)

Get the number of edges in the graph

number_of_nodes(self)

An alias of number_of_vertices().

number_of_vertices(self)

Get the number of vertices in the graph

out_degree(self[, vertex_subset])

Calculates and returns the out-degree of vertices.

renumber(self, source_col, dest_col)

Take a (potentially sparse) set of source and destination vertex ids and renumber the vertices to create a dense set of vertex ids using all values contiguously from 0 to the number of unique vertices - 1.

view_adj_list(self)

Display the adjacency list.

view_edge_list(self)

Display the edge list.

view_transposed_adj_list(self)

Display the transposed adjacency list.

add_adj_list(self, offset_col, index_col, value_col=None, copy=False)

Create the adjacency list representation of a Graph. The passed offset_col and index_col arguments wrap gdf_column objects that represent a graph using the adjacency list format. If value_col is None, an unweighted graph is created. If value_col is not None, an weighted graph is created. If copy is False, this function stores references to the passed objects pointed by offset_col and index_col. If copy is True, this funcion stores references to the deep-copies of the passed objects pointed by offset_col and index_col. If this class instance already stores a graph, invoking this function raises an error. Undirected edges must be stored as directed edges in both directions.

Parameters
offset_colcudf.Series

This cudf.Series wraps a gdf_column of size V + 1 (V: number of vertices). The gdf column contains the offsets for the vertices in this graph. Offsets must be in the range [0, E] (E: number of edges).

index_colcudf.Series

This cudf.Series wraps a gdf_column of size E (E: number of edges). The gdf column contains the destination index for each edge. Destination indices must be in the range [0, V) (V: number of vertices).

value_col(optional)cudf.Series

This pointer can be none. If not, this cudf.Series wraps a gdf_column of size E (E: number of edges). The gdf column contains the weight value for each edge. The expected type of the gdf_column element is floating point number.

Examples

>>> import numpy as np
>>> import pytest
>>> from scipy.io import mmread
>>>
>>> import cudf
>>> import cugraph
>>>
>>>
>>> mm_file = '../datasets/karate.mtx'
>>> M = mmread(mm_file).asfptype()
>>> M = M.tocsr()
>>> offsets = cudf.Series(M.indptr)
>>> indices = cudf.Series(M.indices)
>>>
>>> G = cugraph.Graph()
>>> G.add_adj_list(offsets, indices, None)
add_edge_list(self, source_col, dest_col, value_col=None, copy=False)

Create the edge list representation of a Graph. The passed source_col and dest_col arguments wrap gdf_column objects that represent a graph using the edge list format. Source and destination indices must be in the range [0, V) where V is the number of vertices. They must be 32 bit integers. Please refer to cuGraph’s renumbering feature if your input does not match these requierments. When using cudf.read_csv to load a CSV edge list, make sure to set dtype to int32 for the source and destination columns. Undirected edges must be stored as directed edges in both directions. If value_col is None, an unweighted graph is created. If value_col is not None, an weighted graph is created. If copy is False, this function stores references to the passed objects pointed by source_col and dest_col. If copy is True, this funcion stores references to the deep-copies of the passed objects pointed by source_col and dest_col. If this class instance already stores a graph, invoking this function raises an error.

Parameters
source_colcudf.Series

This cudf.Series wraps a gdf_column of size E (E: number of edges). The gdf column contains the source index for each edge. Source indices must be in the range [0, V) (V: number of vertices). Source indices must be 32 bit integers.

dest_colcudf.Series

This cudf.Series wraps a gdf_column of size E (E: number of edges). The gdf column contains the destination index for each edge. Destination indices must be in the range [0, V) (V: number of vertices). Destination indices must be 32 bit integers.

value_col(optional)cudf.Series

This pointer can be none. If not, this cudf.Series wraps a gdf_column of size E (E: number of edges). The gdf column contains the weight value for each edge. The expected type of the gdf_column element is floating point number.

Examples

>>> import numpy as np
>>> import pytest
>>> from scipy.io import mmread
>>>
>>> import cudf
>>> import cugraph
>>>
>>>
>>> mm_file = '../datasets/karate.mtx'
>>> M = mmread(mm_file).asfptype()
>>> sources = cudf.Series(M.row)
>>> destinations = cudf.Series(M.col)
>>>
>>> G = cugraph.Graph()
>>> G.add_edge_list(sources, destinations, None)
add_transposed_adj_list(self)

Compute the transposed adjacency list from the edge list and add it to the existing graph.

clear(self)

Empty this graph. This function is added for NetworkX compatibility.

degree(self, vertex_subset=None)

Calculates and returns the degree of vertices. Vertex degree is the number of edges adjacent to that vertex.

Parameters
vertex_subset(optional, default=all vertices)cudf.Series or iterable container

A container of vertices for displaying corresponding degree

Returns
dfcudf.DataFrame
GPU data frame of size N (the default) or the size of the given vertices (vertex_subset)
containing the degree. The ordering is relative to the adjacency list, or that
given by the specified vertex_subset.
df[‘vertex’]: The vertex IDs (will be identical to vertex_subset if specified)
df[‘degree’]: The computed degree of the corresponding vertex

Examples

>>> import numpy as np
>>> import pytest
>>> from scipy.io import mmread
>>>
>>> import cudf
>>> import cugraph
>>> mm_file = '/datasets/networks/karate.mtx'
>>> M = mmread(mm_file).asfptype()
>>> sources = cudf.Series(M.row)
>>> destinations = cudf.Series(M.col)
>>>
>>> G = cugraph.Graph()
>>> G.add_edge_list(sources, destinations)
>>> degree_df = G.degree([0,9,12])
delete_adj_list(self)

Delete the adjacency list.

delete_edge_list(self)

Delete the edge list.

delete_transposed_adj_list(self)

Delete the transposed adjacency list.

get_two_hop_neighbors(self)

Return a dataframe containing vertex pairs such that each pair of vertices is connected by a path of two hops in the graph. The resulting pairs are returned in sorted order.

Returns
Two hop neighborscudf.DataFrame

df[‘first’] the first vertex id of a pair df[‘second’] the second vertex id of a pair

in_degree(self, vertex_subset=None)

Calculates and returns the in-degree of vertices. Vertex in-degree is the number of edges pointing in to the vertex.

Parameters
vertex_subset(optional, default=all vertices)cudf.Series or iterable container

A container of vertices for displaying corresponding in-degree

Returns
dfcudf.DataFrame
GPU data frame of size N (the default) or the size of the given vertices (vertex_subset)
containing the in_degree. The ordering is relative to the adjacency list, or that
given by the specified vertex_subset.
df[‘vertex’]: The vertex IDs (will be identical to vertex_subset if specified)
df[‘degree’]: The computed in-degree of the corresponding vertex

Examples

>>> import numpy as np
>>> import pytest
>>> from scipy.io import mmread
>>>
>>> import cudf
>>> import cugraph
>>> mm_file = '/datasets/networks/karate.mtx'
>>> M = mmread(mm_file).asfptype()
>>> sources = cudf.Series(M.row)
>>> destinations = cudf.Series(M.col)
>>>
>>> G = cugraph.Graph()
>>> G.add_edge_list(sources, destinations)
>>> in_degree_df = G.in_degree([0,9,12])
number_of_edges(self)

Get the number of edges in the graph

number_of_nodes(self)

An alias of number_of_vertices(). This function is added for NetworkxX compatibility.

number_of_vertices(self)

Get the number of vertices in the graph

out_degree(self, vertex_subset=None)

Calculates and returns the out-degree of vertices. Vertex out-degree is the number of edges pointing out from the vertex.

Parameters
vertex_subset(optional, default=all vertices)cudf.Series or iterable container

A container of vertices for displaying corresponding out-degree

Returns
dfcudf.DataFrame
GPU data frame of size N (the default) or the size of the given vertices (vertex_subset)
containing the out_degree. The ordering is relative to the adjacency list, or that
given by the specified vertex_subset.
df[‘vertex’]: The vertex IDs (will be identical to vertex_subset if specified)
df[‘degree’]: The computed out-degree of the corresponding vertex

Examples

>>> import numpy as np
>>> import pytest
>>> from scipy.io import mmread
>>>
>>> import cudf
>>> import cugraph
>>> mm_file = '/datasets/networks/karate.mtx'
>>> M = mmread(mm_file).asfptype()
>>> sources = cudf.Series(M.row)
>>> destinations = cudf.Series(M.col)
>>>
>>> G = cugraph.Graph()
>>> G.add_edge_list(sources, destinations)
>>> out_degree_df = G.out_degree([0,9,12])
renumber(self, source_col, dest_col)

Take a (potentially sparse) set of source and destination vertex ids and renumber the vertices to create a dense set of vertex ids using all values contiguously from 0 to the number of unique vertices - 1.

Input columns can be either int64 or int32. The output will be mapped to int32, since many of the cugraph functions are limited to int32. If the number of unique values in source_col and dest_col > 2^31-1 then this function will return an error.

Return from this call will be three cudf Series - the renumbered source_col, the renumbered dest_col and a numbering map that maps the new ids to the original ids.

Parameters
source_colcudf.Series

This cudf.Series wraps a gdf_column of size E (E: number of edges). The gdf column contains the source index for each edge. Source indices must be an integer type.

dest_colcudf.Series

This cudf.Series wraps a gdf_column of size E (E: number of edges). The gdf column contains the destination index for each edge. Destination indices must be an integer type.

Examples

>>> import numpy as np
>>> import pytest
>>> from scipy.io import mmread
>>>
>>> import cudf
>>> import cugraph
>>>
>>>
>>> mm_file = '../datasets/karate.mtx'
>>> M = mmread(mm_file).asfptype()
>>> sources = cudf.Series(M.row)
>>> destinations = cudf.Series(M.col)
>>>
>>> G = cugraph.Graph()
>>> src_r, dst_r, numbering = G.renumber(sources, destinations)
view_adj_list(self)

Display the adjacency list. Compute it if needed.

view_edge_list(self)

Display the edge list. Compute it if needed.

view_transposed_adj_list(self)

Display the transposed adjacency list. Compute it if needed.

Pagerank

cugraph.pagerank(G, alpha=0.85, personalization=None, max_iter=100, tol=1.0e-5, nstart=None)

Find the PageRank vertex values for a graph. cuGraph computes an approximation of the Pagerank eigenvector using the power method. The number of iterations depends on the properties of the network itself; it increases when the tolerance descreases and/or alpha increases toward the limiting value of 1. The user is free to use default values or to provide inputs for the initial guess, tolerance and maximum number of iterations.

Parameters
graphcuGraph.Graph

cuGraph graph descriptor, should contain the connectivity information as an edge list (edge weights are not used for this algorithm). The transposed adjacency list will be computed if not already present.

alphafloat

The damping factor alpha represents the probability to follow an outgoing edge, standard value is 0.85. Thus, 1.0-alpha is the probability to “teleport” to a random vertex. Alpha should be greater than 0.0 and strictly lower than 1.0.

personalizationcudf.Dataframe

GPU Dataframe containing the personalizatoin information. personalization[‘vertex’]: Subset of vertices of graph for personalization personalization[‘values’]: Personalization values for vertices

max_iterint

The maximum number of iterations before an answer is returned. This can be used to limit the execution time and do an early exit before the solver reaches the convergence tolerance. If this value is lower or equal to 0 cuGraph will use the default value, which is 100.

tolerancefloat

Set the tolerance the approximation, this parameter should be a small magnitude value. The lower the tolerance the better the approximation. If this value is 0.0f, cuGraph will use the default value which is 1.0E-5. Setting too small a tolerance can lead to non-convergence due to numerical roundoff. Usually values between 0.01 and 0.00001 are acceptable.

nstartcudf.Dataframe

GPU Dataframe containing the initial guess for pagerank. nstart[‘vertex’]: Subset of vertices of graph for initial guess for pagerank values nstart[‘values’]: Pagerank values for vertices

Returns
PageRankcudf.DataFrame

GPU data frame containing two cudf.Series of size V: the vertex identifiers and the corresponding PageRank values.

Examples

>>> M = read_mtx_file(graph_file)
>>> sources = cudf.Series(M.row)
>>> destinations = cudf.Series(M.col)
>>> G = cuGraph.Graph()
>>> G.add_edge_list(sources,destinations,None)
>>> pr = cuGraph.pagerank(G, alpha = 0.85, max_iter = 500, tol = 1.0e-05)

Bfs

cugraph.bfs(G, start, directed=True)

Find the distances and predecessors for a breadth first traversal of a graph.

Parameters
Gcugraph.graph

cuGraph graph descriptor, should contain the connectivity information as an adjacency list.

startInteger

The index of the graph vertex from which the traversal begins

directedbool

Indicates whether the graph in question is a directed graph, or whether each edge has a corresponding reverse edge. (Allows optimizations if the graph is undirected)

Returns
dfcudf.DataFrame

df[‘vertex’][i] gives the vertex id of the i’th vertex df[‘distance’][i] gives the path distance for the i’th vertex from the starting vertex df[‘predecessor’][i] gives for the i’th vertex the vertex it was reached from in the traversal

Examples

>>> M = read_mtx_file(graph_file)
>>> sources = cudf.Series(M.row)
>>> destinations = cudf.Series(M.col)
>>> G = cuGraph.Graph()
>>> G.add_edge_list(sources,destinations,none)
>>> dist, pred = cuGraph.bfs(G, 0, false)

Jaccard

Louvain

cugraph.nvLouvain(input_graph)

Compute the modularity optimizing partition of the input graph using the Louvain heuristic

Parameters
input_graphcuGraph.Graph

cuGraph graph descriptor, should contain the connectivity information as an edge list. The adjacency list will be computed if not already present. The graph should be undirected where an undirected edge is represented by a directed edge in both direction.

Returns
louvain_parts, modularity_scorecudf.DataFrame
louvain_parts: GPU data frame of size V containing two columns: the vertex id

and the partition id it is assigned to.

modularity_score: a double value containing the modularity score of the partitioning

Examples

>>> M = read_mtx_file(graph_file)
>>> sources = cudf.Series(M.row)
>>> destinations = cudf.Series(M.col)
>>> G = cuGraph.Graph()
>>> G.add_edge_list(sources,destinations,None)
>>> louvain_parts, modularity_score = cuGraph.louvain(G)

Grmat

cugraph.grmat_gen(argv)

Spectral Clustering

cugraph.spectralBalancedCutClustering(G, num_clusters, num_eigen_vects=2, evs_tolerance=.00001, evs_max_iter=100, kmean_tolerance=.00001, kmean_max_iter=100)

Compute a clustering/partitioning of the given graph using the spectral balanced cut method.

Parameters
GcuGraph.Graph

cuGraph graph descriptor

num_clustersinteger

Specifies the number of clusters to find

num_eigen_vectsinteger

Specifies the number of eigenvectors to use. Must be lower or equal to num_clusters.

evs_tolerance: float

Specifies the tolerance to use in the eigensolver

evs_max_iter: integer

Specifies the maximum number of iterations for the eigensolver

kmean_tolerance: float

Specifies the tolerance to use in the k-means solver

kmean_max_iter: integer

Specifies the maximum number of iterations for the k-means solver

Returns
DFGPU data frame containing two cudf.Series of size V: the vertex identifiers and the
corresponding cluster assignments.

DF[‘vertex’] contains the vertex identifiers DF[‘cluster’] contains the cluster assignments

cugraph.spectralModularityMaximizationClustering(G, num_clusters, num_eigen_vects=2, evs_tolerance=.00001, evs_max_iter=100, kmean_tolerance=.00001, kmean_max_iter=100)

Compute a clustering/partitioning of the given graph using the spectral modularity maximization method.

Parameters
GcuGraph.Graph

cuGraph graph descriptor

num_clustersinteger

Specifies the number of clusters to find

num_eigen_vectsinteger

Specifies the number of eigenvectors to use. Must be lower or equal to num_clusters

evs_tolerance: float

Specifies the tolerance to use in the eigensolver

evs_max_iter: integer

Specifies the maximum number of iterations for the eigensolver

kmean_tolerance: float

Specifies the tolerance to use in the k-means solver

kmean_max_iter: integer

Specifies the maximum number of iterations for the k-means solver

Returns
Clusteringcudf.DataFrame

DF[‘vertex’] contains the vertex identifiers DF[‘cluster’] contains the cluster assignments

cugraph.analyzeClustering_modularity(G, n_clusters, clustering)

Compute the modularity score for a partitioning/clustering

Parameters
GcuGraph.Graph

cuGraph graph descriptor

n_clustersinteger

Specifies the number of clusters in the given clustering

clusteringcudf.Series

The cluster assignment to analyze.

Returns
scorefloat

The computed modularity score

cugraph.analyzeClustering_edge_cut(G, n_clusters, clustering)

Compute the edge cut score for a partitioning/clustering

Parameters
GcuGraph.Graph

cuGraph graph descriptor

n_clustersinteger

Specifies the number of clusters in the given clustering

clusteringcudf.Series

The cluster assignment to analyze.

Returns
scorefloat

The computed edge cut score

cugraph.analyzeClustering_ratio_cut(G, n_clusters, clustering)

Compute the ratio cut score for a partitioning/clustering

Parameters
GcuGraph.Graph

cuGraph graph descriptor

n_clustersinteger

Specifies the number of clusters in the given clustering

clusteringcudf.Series

The cluster assignment to analyze.

Returns
scorefloat

The computed ratio cut score

Sssp

cugraph.sssp(G, source)

Compute the distance and predecessors for shortest paths from the specified source to all the vertices in the graph. The distances column will store the distance from the source to each vertex. The predecessors column will store each vertex’s predecessor in the shortest path. Vertices that are unreachable will have a distance of infinity denoted by the maximum value of the data type and the predecessor set as -1. The source vertex’s predecessor is also set to -1. Graphs with negative weight cycles are not supported.

Parameters
graphcuGraph.Graph

cuGraph graph descriptor with connectivity information. Edge weights, if present, should be single or double precision floating point values

sourceint

Index of the source vertex

Returns
dfcudf.DataFrame

df[‘vertex’][i] gives the vertex id of the i’th vertex df[‘distance’][i] gives the path distance for the i’th vertex from the starting vertex df[‘predecessor’][i] gives the vertex id of the vertex that was reached before the i’th vertex in the traversal

Examples

>>> M = read_mtx_file(graph_file)
>>> sources = cudf.Series(M.row)
>>> destinations = cudf.Series(M.col)
>>> G = cuGraph.Graph()
>>> G.add_edge_list(sources,destinations,None)
>>> distances = cuGraph.sssp(G, source)