Community#

Triangle Counting#

cugraph_error_code_t cugraph_triangle_count(const cugraph_resource_handle_t *handle, cugraph_graph_t *graph, const cugraph_type_erased_device_array_view_t *start, bool_t do_expensive_check, cugraph_triangle_count_result_t **result, cugraph_error_t **error)#

Triangle Counting.

Parameters:

  • handle – [in] Handle for accessing resources

  • graph – [in] Pointer to graph. NOTE: Graph might be modified if the storage needs to be transposed

  • start – [in] Device array of vertices we want to count triangles for. If NULL the entire set of vertices in the graph is processed

  • do_expensive_check – [in] A flag to run expensive checks for input arguments (if set to true)

  • result – [out] Output from the triangle_count call

  • error – [out] Pointer to an error object storing details of any error. Will be populated if error code is not CUGRAPH_SUCCESS

Returns:

error code

Louvain#

cugraph_error_code_t cugraph_louvain(const cugraph_resource_handle_t *handle, cugraph_graph_t *graph, size_t max_level, double threshold, double resolution, bool_t do_expensive_check, cugraph_hierarchical_clustering_result_t **result, cugraph_error_t **error)#

Compute Louvain.

Parameters:

  • handle – [in] Handle for accessing resources

  • graph – [in] Pointer to graph. NOTE: Graph might be modified if the storage needs to be transposed

  • max_level – [in] Maximum level in hierarchy

  • threshold – [in] Threshold parameter, defines convergence at each level of hierarchy

  • resolution – [in] Resolution parameter (gamma) in modularity formula. This changes the size of the communities. Higher resolutions lead to more smaller communities, lower resolutions lead to fewer larger communities.

  • do_expensive_check – [in] A flag to run expensive checks for input arguments (if set to true)

  • result – [out] Output from the Louvain call

  • error – [out] Pointer to an error object storing details of any error. Will be populated if error code is not CUGRAPH_SUCCESS

Returns:

error code

Leiden#

cugraph_error_code_t cugraph_leiden(const cugraph_resource_handle_t *handle, cugraph_rng_state_t *rng_state, cugraph_graph_t *graph, size_t max_level, double resolution, double theta, bool_t do_expensive_check, cugraph_hierarchical_clustering_result_t **result, cugraph_error_t **error)#

Compute Leiden.

Parameters:

  • handle – [in] Handle for accessing resources

  • graph – [in] Pointer to graph. NOTE: Graph might be modified if the storage needs to be transposed

  • rng_state – [inout] State of the random number generator, updated with each call

  • max_level – [in] Maximum level in hierarchy

  • resolution – [in] Resolution parameter (gamma) in modularity formula. This changes the size of the communities. Higher resolutions lead to more smaller communities, lower resolutions lead to fewer larger communities.

  • theta – [in] (optional) The value of the parameter to scale modularity gain in Leiden refinement phase. It is used to compute the probability of joining a random leiden community. Called theta in the Leiden algorithm.

  • do_expensive_check – [in] A flag to run expensive checks for input arguments (if set to true)

  • result – [out] Output from the Leiden call

  • error – [out] Pointer to an error object storing details of any error. Will be populated if error code is not CUGRAPH_SUCCESS

Returns:

error code

ECG#

cugraph_error_code_t cugraph_ecg(const cugraph_resource_handle_t *handle, cugraph_rng_state_t *rng_state, cugraph_graph_t *graph, double min_weight, size_t ensemble_size, size_t max_level, double threshold, double resolution, bool_t do_expensive_check, cugraph_hierarchical_clustering_result_t **result, cugraph_error_t **error)#

Compute ECG clustering.

Parameters:

  • handle – [in] Handle for accessing resources

  • rng_state – [inout] State of the random number generator, updated with each call

  • graph – [in] Pointer to graph. NOTE: Graph might be modified if the storage needs to be transposed

  • min_weight – [in] Minimum edge weight in final graph

  • ensemble_size – [in] The number of Louvain iterations to run

  • max_level – [in] Maximum level in hierarchy for final Louvain

  • threshold – [in] Threshold parameter, defines convergence at each level of hierarchy for final Louvain

  • resolution – [in] Resolution parameter (gamma) in modularity formula. This changes the size of the communities. Higher resolutions lead to more smaller communities, lower resolutions lead to fewer larger communities.

  • do_expensive_check – [in] A flag to run expensive checks for input arguments (if set to true)

  • result – [out] Output from the Louvain call

  • error – [out] Pointer to an error object storing details of any error. Will be populated if error code is not CUGRAPH_SUCCESS

Returns:

error code

Extract Egonet#

cugraph_error_code_t cugraph_extract_ego(const cugraph_resource_handle_t *handle, cugraph_graph_t *graph, const cugraph_type_erased_device_array_view_t *source_vertices, size_t radius, bool_t do_expensive_check, cugraph_induced_subgraph_result_t **result, cugraph_error_t **error)#

Extract ego graphs.

Parameters:

  • handle – [in] Handle for accessing resources

  • graph – [in] Pointer to graph. NOTE: Graph might be modified if the storage needs to be transposed

  • source_vertices – [in] Device array of vertices we want to extract egonets for.

  • radius – [in] The number of hops to go out from each source vertex

  • do_expensive_check – [in] A flag to run expensive checks for input arguments (if set to true)

  • result – [out] Opaque object containing the extracted subgraph

  • error – [out] Pointer to an error object storing details of any error. Will be populated if error code is not CUGRAPH_SUCCESS

Returns:

error code

Balanced Cut#

cugraph_error_code_t cugraph_balanced_cut_clustering(const cugraph_resource_handle_t *handle, cugraph_graph_t *graph, size_t n_clusters, size_t n_eigenvectors, double evs_tolerance, int evs_max_iterations, double k_means_tolerance, int k_means_max_iterations, bool_t do_expensive_check, cugraph_clustering_result_t **result, cugraph_error_t **error)#

Balanced cut clustering.

NOTE: This currently wraps the legacy balanced cut clustering implementation and is only available in Single GPU implementation.

Parameters:

  • handle – [in] Handle for accessing resources

  • graph – [in] Pointer to graph. NOTE: Graph might be modified if the storage needs to be transposed

  • n_clusters – [in] The desired number of clusters

  • n_eigenvectors – [in] The number of eigenvectors to use

  • evs_tolerance – [in] The tolerance to use for the eigenvalue solver

  • evs_max_iterations – [in] The maximum number of iterations of the eigenvalue solver

  • k_means_tolerance – [in] The tolerance to use for the k-means solver

  • k_means_max_iterations – [in] The maximum number of iterations of the k-means solver

  • do_expensive_check – [in] A flag to run expensive checks for input arguments (if set to true)

  • result – [out] Opaque object containing the clustering result

  • error – [out] Pointer to an error object storing details of any error. Will be populated if error code is not CUGRAPH_SUCCESS

Returns:

error code

Spectral Clustering - Modularity Maximization#

cugraph_error_code_t cugraph_spectral_modularity_maximization(const cugraph_resource_handle_t *handle, cugraph_graph_t *graph, size_t n_clusters, size_t n_eigenvectors, double evs_tolerance, int evs_max_iterations, double k_means_tolerance, int k_means_max_iterations, bool_t do_expensive_check, cugraph_clustering_result_t **result, cugraph_error_t **error)#

Spectral clustering.

NOTE: This currently wraps the legacy spectral clustering implementation and is only available in Single GPU implementation.

Parameters:

  • handle – [in] Handle for accessing resources

  • graph – [in] Pointer to graph. NOTE: Graph might be modified if the storage needs to be transposed

  • n_clusters – [in] The desired number of clusters

  • n_eigenvectors – [in] The number of eigenvectors to use

  • evs_tolerance – [in] The tolerance to use for the eigenvalue solver

  • evs_max_iterations – [in] The maximum number of iterations of the eigenvalue solver

  • k_means_tolerance – [in] The tolerance to use for the k-means solver

  • k_means_max_iterations – [in] The maximum number of iterations of the k-means solver

  • do_expensive_check – [in] A flag to run expensive checks for input arguments (if set to true)

  • result – [out] Opaque object containing the clustering result

  • error – [out] Pointer to an error object storing details of any error. Will be populated if error code is not CUGRAPH_SUCCESS

Returns:

error code

cugraph_error_code_t cugraph_analyze_clustering_modularity(const cugraph_resource_handle_t *handle, cugraph_graph_t *graph, size_t n_clusters, const cugraph_type_erased_device_array_view_t *vertices, const cugraph_type_erased_device_array_view_t *clusters, double *score, cugraph_error_t **error)#

Compute modularity of the specified clustering.

NOTE: This currently wraps the legacy spectral modularity implementation and is only available in Single GPU implementation.

Parameters:

  • handle – [in] Handle for accessing resources

  • graph – [in] Pointer to graph. NOTE: Graph might be modified if the storage needs to be transposed

  • n_clusters – [in] The desired number of clusters

  • vertices – [in] Vertex ids from the clustering result

  • clusters – [in] Cluster ids from the clustering result

  • score – [out] The modularity score for this clustering

  • error – [out] Pointer to an error object storing details of any error. Will be populated if error code is not CUGRAPH_SUCCESS

Returns:

error code

Spectral Clustering - Edge Cut#

cugraph_error_code_t cugraph_analyze_clustering_edge_cut(const cugraph_resource_handle_t *handle, cugraph_graph_t *graph, size_t n_clusters, const cugraph_type_erased_device_array_view_t *vertices, const cugraph_type_erased_device_array_view_t *clusters, double *score, cugraph_error_t **error)#

Compute edge cut of the specified clustering.

NOTE: This currently wraps the legacy spectral edge cut implementation and is only available in Single GPU implementation.

Parameters:

  • handle – [in] Handle for accessing resources

  • graph – [in] Pointer to graph. NOTE: Graph might be modified if the storage needs to be transposed

  • n_clusters – [in] The desired number of clusters

  • vertices – [in] Vertex ids from the clustering result

  • clusters – [in] Cluster ids from the clustering result

  • score – [out] The edge cut score for this clustering

  • error – [out] Pointer to an error object storing details of any error. Will be populated if error code is not CUGRAPH_SUCCESS

Returns:

error code

cugraph_error_code_t cugraph_analyze_clustering_ratio_cut(const cugraph_resource_handle_t *handle, cugraph_graph_t *graph, size_t n_clusters, const cugraph_type_erased_device_array_view_t *vertices, const cugraph_type_erased_device_array_view_t *clusters, double *score, cugraph_error_t **error)#

Compute ratio cut of the specified clustering.

NOTE: This currently wraps the legacy spectral ratio cut implementation and is only available in Single GPU implementation.

Parameters:

  • handle – [in] Handle for accessing resources

  • graph – [in] Pointer to graph. NOTE: Graph might be modified if the storage needs to be transposed

  • n_clusters – [in] The desired number of clusters

  • vertices – [in] Vertex ids from the clustering result

  • clusters – [in] Cluster ids from the clustering result

  • score – [out] The ratio cut score for this clustering

  • error – [out] Pointer to an error object storing details of any error. Will be populated if error code is not CUGRAPH_SUCCESS

Returns:

error code

Community Support Functions#

cugraph_type_erased_device_array_view_t *cugraph_triangle_count_result_get_vertices(cugraph_triangle_count_result_t *result)#

Get triangle counting vertices.

cugraph_type_erased_device_array_view_t *cugraph_triangle_count_result_get_counts(cugraph_triangle_count_result_t *result)#

Get triangle counting counts.

void cugraph_triangle_count_result_free(cugraph_triangle_count_result_t *result)#

Free a triangle count result.

Parameters:

result – [in] The result from a sampling algorithm

cugraph_type_erased_device_array_view_t *cugraph_hierarchical_clustering_result_get_vertices(cugraph_hierarchical_clustering_result_t *result)#

Get hierarchical clustering vertices.

cugraph_type_erased_device_array_view_t *cugraph_hierarchical_clustering_result_get_clusters(cugraph_hierarchical_clustering_result_t *result)#

Get hierarchical clustering clusters.

double cugraph_hierarchical_clustering_result_get_modularity(cugraph_hierarchical_clustering_result_t *result)#

Get modularity.

void cugraph_hierarchical_clustering_result_free(cugraph_hierarchical_clustering_result_t *result)#

Free a hierarchical clustering result.

Parameters:

result – [in] The result from a sampling algorithm

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