use-lynxkite-boxes— agent skill

Available boxes

The following boxes are available for use in your workflows. Each box corresponds to a specific operation or function that can be used to build your workflow. For detailed information on each box, please refer to the individual box documentation in the references folder.

Cross-entropy loss: Cross-entropy loss for usage information, see references/no_op.md

Drop first n: Drop first n for usage information, see references/no_op.md

Graph conv: Graph conv for usage information, see references/no_op.md

Heterogeneous graph conv: Heterogeneous graph conv for usage information, see references/no_op.md

Optimizer: Optimizer for usage information, see references/no_op.md

Output: Output for usage information, see references/no_op.md

Pick element by constant: Pick element by constant for usage information, see references/no_op.md

Pick element by index: Pick element by index for usage information, see references/no_op.md

Recurrent chain: Recurrent chain for usage information, see references/no_op.md

Repeat: Repeat for usage information, see references/no_op.md

Take first n: Take first n for usage information, see references/no_op.md

Triplet margin loss: Triplet margin loss for usage information, see references/no_op.md

View tables: View tables for usage information, see references/view_tables.md

Export to file: Exports a DataFrame to a file. for usage information, see references/export_to_file.md

Graph from OSM: Graph from OSM for usage information, see references/import_osm.md

Import CSV: Imports a CSV file. for usage information, see references/import_csv.md

Import file: Read the contents of the a file into a Bundle. for usage information, see references/import_file.md

Import GraphML: Imports a GraphML file. for usage information, see references/import_graphml.md

Import Parquet: Imports a Parquet file. for usage information, see references/import_parquet.md

Aggregate on neighbors: Aggregate on neighbors for usage information, see references/aggregate_on_neighbors.md

Connect nodes on attribute: Creates edges between nodes from table1 and table2 if the two attributes of the node are equal. for usage information, see references/connect_nodes.md

Define Edges: Define edges between node tables for usage information, see references/define_edges.md

Degree: Degree for usage information, see references/degree.md

Discard loop edges: Discard loop edges for usage information, see references/discard_loop_edges.md

Discard parallel edges: Discard parallel edges for usage information, see references/discard_parallel_edges.md

Graph from edge list: Graph from edge list for usage information, see references/graph_from_edge_list.md

Merge: Merge multiple inputs for usage information, see references/merge.md

Sample graph: Takes a (preferably connected) subgraph. for usage information, see references/sample_graph.md

Define model: Trains the selected model on the selected dataset. Most training parameters are set in the model definition. for usage information, see references/define_model.md

Model inference: Executes a trained model. for usage information, see references/model_inference.md

Train model: Trains the selected model on the selected dataset. for usage information, see references/train_model.md

Train/test split: Splits a dataframe in the bundle into separate "_train" and "_test" dataframes. for usage information, see references/train_test_split.md

Train/test/validation split: Splits a dataframe in the bundle into separate "_train", "_test" and "_val" dataframes. for usage information, see references/train_test_val_split.md

View loss: View loss for usage information, see references/view_loss.md

View vectors: View vectors for usage information, see references/view_vectors.md

Define inductive PyKEEN model: Defines an InductiveNodePiece model (with an optional GNN message passing layer) for inductive link prediction tasks. for usage information, see references/get_inductive_model.md

Define PyKEEN model: Defines a PyKEEN model based on the selected model type. for usage information, see references/define_pykeen_model.md

Define PyKEEN model with node attributes: Defines a PyKEEN model capable of using numeric literals as node attributes. for usage information, see references/def_pykeen_with_attributes.md

Evaluate inductive model: Evaluate inductive model for usage information, see references/eval_inductive_model.md

Evaluate model: Evaluates the given model on the test set using the specified evaluator type. for usage information, see references/evaluate.md

Extract embeddings from PyKEEN model: Extract embeddings from PyKEEN model for usage information, see references/extract_from_pykeen.md

Full prediction: Warning: This prediction can be a very expensive operation! for usage information, see references/full_predict.md

Import inductive dataset: Imports an inductive dataset from the PyKEEN library. for usage information, see references/import_inductive_dataset.md

Import PyKEEN dataset: Imports a dataset from the PyKEEN library. for usage information, see references/import_pykeen_dataset_path.md

Split inductive dataset: Splits incoming data into 4 subsets. Transductive training on which training should be run, inductive inference on which during training inference is done. for usage information, see references/inductively_split_dataset.md

Target prediction: Leave the target prediction field empty for usage information, see references/target_predict.md

Train embedding model: Train embedding model for usage information, see references/train_embedding_model.md

Train inductive model: Train inductive model for usage information, see references/train_inductive_pykeen_model.md

Triples prediction: Triples prediction for usage information, see references/triple_predict.md

View early stopping metric: View early stopping metric for usage information, see references/view_early_stopping.md

Cypher: Run a Cypher query on the graph in the bundle. Save the results as a new DataFrame. for usage information, see references/cypher.md

SQL: Run a SQL query on the DataFrames in the bundle. Save the results as a new DataFrame. for usage information, see references/sql.md

Add rank attribute: Sorts the rows by the given attribute in the given order and creates a new column with the rank of the row for usage information, see references/add_rank.md

Derive property: Derive property for usage information, see references/derive_property.md

Enter table data: Enter table data as CSV. The first row should contain column names. for usage information, see references/enter_table_data.md

Filter with formula: Removes all rows where the formula(https://numexpr.readthedocs.io/en/latest/user_guide.html#supported-functions) evaluates to false for usage information, see references/filter_with_formula.md

Join tables: Join/merge dataframes from two bundles. for usage information, see references/join_tables.md

Rename table: Assigns a new name to the table for usage information, see references/rename_table.md

Sample table: Sample table for usage information, see references/sample_table.md

Select Table: Select Table for usage information, see references/select_table.md

Vector from attribute pair: Creates a new column with vectors that contain the two attributes for usage information, see references/vector_from_attribute_pair.md

Bar chart: Bar chart for usage information, see references/bar_chart.md

Binned graph visualization: Nodes binned together by x and y are aggregated into one node. for usage information, see references/binned_graph_visualization.md

Histogram: Histogram for usage information, see references/histogram.md

Scatter plot: Scatter plot for usage information, see references/scatter_plot.md

Visualize graph: Visualize graph for usage information, see references/visualize_graph.md

Activation: Activation for usage information, see references/activation.md

Add: Add for usage information, see references/<lambda>.md

Attention: Attention for usage information, see references/attention.md

Binary cross-entropy with logits loss: Binary cross-entropy with logits loss for usage information, see references/binary_cross_entropy_loss.md

Concatenate: Concatenate for usage information, see references/concatenate.md

Constant vector: Constant vector for usage information, see references/constant_vector.md

Cos: Cos for usage information, see references/<lambda>.md

Dropout: Dropout for usage information, see references/dropout.md

Embedding: Embedding for usage information, see references/embedding.md

Exp: Exp for usage information, see references/<lambda>.md

Input: graph edges: The edges of a graph as input. A 2xE tensor of src/dst indices. Not batched. for usage information, see references/graph_edges_input.md

Input: sequential: An input tensor with a sequence for each sample. for usage information, see references/sequential_input.md

Input: tensor: An input tensor. for usage information, see references/tensor_input.md

LayerNorm: LayerNorm for usage information, see references/layernorm.md

Linear: Linear for usage information, see references/linear.md

Log: Log for usage information, see references/<lambda>.md

LSTM: LSTM for usage information, see references/lstm.md

Mean pool: Mean pool for usage information, see references/mean_pool.md

MSE loss: MSE loss for usage information, see references/mse_loss.md

Multiply: Multiply for usage information, see references/<lambda>.md

Neural ODE with MLP: A neural ODE for predicting a 1-dimensional value over time, using an MLP to model the derivative. for usage information, see references/neural_ode_mlp.md

Sin: Sin for usage information, see references/<lambda>.md

Softmax: Softmax for usage information, see references/softmax.md

Subtract: Subtract for usage information, see references/<lambda>.md

Blur: Blur for usage information, see references/blur.md

Crop: Crop for usage information, see references/crop.md

Detail: Detail for usage information, see references/detail.md

Edge enhance: Edge enhance for usage information, see references/edge_enhance.md

Flip horizontally: Flip horizontally for usage information, see references/flip_horizontally.md

Flip vertically: Flip vertically for usage information, see references/flip_vertically.md

Open image: Open image for usage information, see references/open_image.md

Save image: Save image for usage information, see references/save_image.md

To grayscale: To grayscale for usage information, see references/to_grayscale.md

View image: View image for usage information, see references/view_image.md

Average neighbor degree: Returns the average degree of the neighborhood of each node. for usage information, see references/average_neighbor_degree.md

Find asteroidal triple: Find an asteroidal triple in the given graph. for usage information, see references/find_asteroidal_triple.md

Is AT-free: Check if a graph is AT-free. for usage information, see references/is_at_free.md

Is bipartite: Returns True if graph G is bipartite, False if not. for usage information, see references/is_bipartite.md

Complete bipartite graph: Returns the complete bipartite graph K_{n_1,n_2}. for usage information, see references/complete_bipartite_graph.md

Local bridges: Iterate over local bridges of G optionally computing the span for usage information, see references/local_bridges.md

Tree broadcast center: Return the broadcast center of a tree. for usage information, see references/tree_broadcast_center.md

Tree broadcast time: Return the minimum broadcast time of a (node in a) tree. for usage information, see references/tree_broadcast_time.md

Betweenness centrality: Compute the shortest-path betweenness centrality for nodes. for usage information, see references/betweenness_centrality.md

Edge betweenness centrality: Compute betweenness centrality for edges. for usage information, see references/edge_betweenness_centrality.md

Closeness centrality: Compute closeness centrality for nodes. for usage information, see references/closeness_centrality.md

Degree centrality: Compute the degree centrality for nodes. for usage information, see references/degree_centrality.md

In degree centrality: Compute the in-degree centrality for nodes. for usage information, see references/in_degree_centrality.md

Out degree centrality: Compute the out-degree centrality for nodes. for usage information, see references/out_degree_centrality.md

Dispersion: Calculate dispersion between u and v in G. for usage information, see references/dispersion.md

Eigenvector centrality: Compute the eigenvector centrality for the graph G. for usage information, see references/eigenvector_centrality.md

Eigenvector centrality NumPy: Compute the eigenvector centrality for the graph G. for usage information, see references/eigenvector_centrality_numpy.md

Group betweenness centrality: Compute the group betweenness centrality for a group of nodes. for usage information, see references/group_betweenness_centrality.md

Prominent group: Find the prominent group of size $k$ in graph $G$. The prominence of the for usage information, see references/prominent_group.md

Katz centrality: Compute the Katz centrality for the nodes of the graph G. for usage information, see references/katz_centrality.md

Katz centrality NumPy: Compute the Katz centrality for the graph G. for usage information, see references/katz_centrality_numpy.md

Laplacian centrality: Compute the Laplacian centrality for nodes in the graph G. for usage information, see references/laplacian_centrality.md

Edge load centrality: Compute edge load. for usage information, see references/edge_load_centrality.md

Percolation centrality: Compute the percolation centrality for nodes. for usage information, see references/percolation_centrality.md

Global reaching centrality: Returns the global reaching centrality of a directed graph. for usage information, see references/global_reaching_centrality.md

Second order centrality: Compute the second order centrality for nodes of G. for usage information, see references/second_order_centrality.md

Communicability betweenness centrality: Returns subgraph communicability for all pairs of nodes in G. for usage information, see references/communicability_betweenness_centrality.md

Estrada index: Returns the Estrada index of a the graph G. for usage information, see references/estrada_index.md

Subgraph centrality: Returns subgraph centrality for each node in G. for usage information, see references/subgraph_centrality.md

Subgraph centrality exp: Returns the subgraph centrality for each node of G. for usage information, see references/subgraph_centrality_exp.md

Voterank: Select a list of influential nodes in a graph using VoteRank algorithm for usage information, see references/voterank.md

Chordal graph cliques: Returns all maximal cliques of a chordal graph. for usage information, see references/chordal_graph_cliques.md

Chordal graph treewidth: Returns the treewidth of the chordal graph G. for usage information, see references/chordal_graph_treewidth.md

Complete to chordal graph: Return a copy of G completed to a chordal graph for usage information, see references/complete_to_chordal_graph.md

Is chordal: Checks whether G is a chordal graph. for usage information, see references/is_chordal.md

Enumerate all cliques: Returns all cliques in an undirected graph. for usage information, see references/enumerate_all_cliques.md

Find cliques: Returns all maximal cliques in an undirected graph. for usage information, see references/find_cliques.md

Find cliques recursive: Returns all maximal cliques in a graph. for usage information, see references/find_cliques_recursive.md

Make max clique graph: Returns the maximal clique graph of the given graph. for usage information, see references/make_max_clique_graph.md

Max weight clique: Find a maximum weight clique in G. for usage information, see references/max_weight_clique.md

All triangles: Yields all unique triangles in an undirected graph. for usage information, see references/all_triangles.md

Average clustering: Compute the average clustering coefficient for the graph G. for usage information, see references/average_clustering.md

Clustering: Compute the clustering coefficient for nodes. for usage information, see references/clustering.md

Generalized degree: Compute the generalized degree for nodes. for usage information, see references/generalized_degree.md

Square clustering: Compute the squares clustering coefficient for nodes. for usage information, see references/square_clustering.md

Transitivity: Compute graph transitivity, the fraction of all possible triangles for usage information, see references/transitivity.md

Triangles: Compute the number of triangles. for usage information, see references/triangles.md

Equitable color: Provides an equitable coloring for nodes of G. for usage information, see references/equitable_color.md

Greedy color: Color a graph using various strategies of greedy graph coloring. for usage information, see references/greedy_color.md

Communicability: Returns communicability between all pairs of nodes in G. for usage information, see references/communicability.md

Communicability exp: Returns communicability between all pairs of nodes in G. for usage information, see references/communicability_exp.md

Attracting components: Generates the attracting components in G. for usage information, see references/attracting_components.md

Is attracting component: Returns True if G consists of a single attracting component. for usage information, see references/is_attracting_component.md

Number attracting components: Returns the number of attracting components in G. for usage information, see references/number_attracting_components.md

Articulation points: Yield the articulation points, or cut vertices, of a graph. for usage information, see references/articulation_points.md

Biconnected component edges: Returns a generator of lists of edges, one list for each biconnected for usage information, see references/biconnected_component_edges.md

Biconnected components: Returns a generator of sets of nodes, one set for each biconnected for usage information, see references/biconnected_components.md

Is biconnected: Returns True if the graph is biconnected, False otherwise. for usage information, see references/is_biconnected.md

Connected components: Generate connected components. for usage information, see references/connected_components.md

Is connected: Returns True if the graph is connected, False otherwise. for usage information, see references/is_connected.md

Node connected component: Returns the set of nodes in the component of graph containing node n. for usage information, see references/node_connected_component.md

Number connected components: Returns the number of connected components. for usage information, see references/number_connected_components.md

Is semiconnected: Returns True if the graph is semiconnected, False otherwise. for usage information, see references/is_semiconnected.md

Condensation: Returns the condensation of G. for usage information, see references/condensation.md

Is strongly connected: Test directed graph for strong connectivity. for usage information, see references/is_strongly_connected.md

Kosaraju strongly connected components: Generate nodes in strongly connected components of graph. for usage information, see references/kosaraju_strongly_connected_components.md

Number strongly connected components: Returns number of strongly connected components in graph. for usage information, see references/number_strongly_connected_components.md

Strongly connected components: Generate nodes in strongly connected components of graph. for usage information, see references/strongly_connected_components.md

Is weakly connected: Test directed graph for weak connectivity. for usage information, see references/is_weakly_connected.md

Number weakly connected components: Returns the number of weakly connected components in G. for usage information, see references/number_weakly_connected_components.md

Weakly connected components: Generate weakly connected components of G. for usage information, see references/weakly_connected_components.md

Is k edge connected: Tests to see if a graph is k-edge-connected. for usage information, see references/is_k_edge_connected.md

K edge components: Generates nodes in each maximal k-edge-connected component in G. for usage information, see references/k_edge_components.md

K edge subgraphs: Generates nodes in each maximal k-edge-connected subgraph in G. for usage information, see references/k_edge_subgraphs.md

Core number: Returns the core number for each node. for usage information, see references/core_number.md

k-core: Returns the k-core of G. for usage information, see references/k_core.md

k-corona: Returns the k-corona of G. for usage information, see references/k_corona.md

k-crust: Returns the k-crust of G. for usage information, see references/k_crust.md

k-shell: Returns the k-shell of G. for usage information, see references/k_shell.md

k-truss: Returns the k-truss of G. for usage information, see references/k_truss.md

Onion layers: Returns the layer of each vertex in an onion decomposition of the graph. for usage information, see references/onion_layers.md

Chordless cycles: Find simple chordless cycles of a graph. for usage information, see references/chordless_cycles.md

Cycle basis: Returns a list of cycles which form a basis for cycles of G. for usage information, see references/cycle_basis.md

Find cycle: Returns a cycle found via depth-first traversal. for usage information, see references/find_cycle.md

Girth: Returns the girth of the graph. for usage information, see references/girth.md

Minimum cycle basis: Returns a minimum weight cycle basis for G for usage information, see references/minimum_cycle_basis.md

Recursive simple cycles: Find simple cycles (elementary circuits) of a directed graph. for usage information, see references/recursive_simple_cycles.md

Simple cycles: Find simple cycles (elementary circuits) of a graph. for usage information, see references/simple_cycles.md

Find minimal d-separator: Returns a minimal d-separating set between x and y if possible for usage information, see references/find_minimal_d_separator.md

Is d-separator: Return whether node sets x and y are d-separated by z. for usage information, see references/is_d_separator.md

Is minimal d-separator: Determine if z is a minimal d-separator for x and y. for usage information, see references/is_minimal_d_separator.md

All topological sorts: Returns a generator of _all_ topological sorts of the directed graph G. for usage information, see references/all_topological_sorts.md

Ancestors: Returns all nodes having a path to source in G. for usage information, see references/ancestors.md

Antichains: Generates antichains from a directed acyclic graph (DAG). for usage information, see references/antichains.md

DAG longest path: Returns the longest path in a directed acyclic graph (DAG). for usage information, see references/dag_longest_path.md

DAG longest path length: Returns the longest path length in a DAG for usage information, see references/dag_longest_path_length.md

DAG to branching: Returns a branching representing all (overlapping) paths from for usage information, see references/dag_to_branching.md

Descendants: Returns all nodes reachable from source in G. for usage information, see references/descendants.md

Is aperiodic: Returns True if G is aperiodic. for usage information, see references/is_aperiodic.md

Is directed acyclic graph: Returns True if the graph G is a directed acyclic graph (DAG) or for usage information, see references/is_directed_acyclic_graph.md

Lexicographical topological sort: Generate the nodes in the unique lexicographical topological sort order. for usage information, see references/lexicographical_topological_sort.md

Topological generations: Stratifies a DAG into generations. for usage information, see references/topological_generations.md

Topological sort: Returns a generator of nodes in topologically sorted order. for usage information, see references/topological_sort.md

Transitive closure: Returns transitive closure of a graph for usage information, see references/transitive_closure.md

Transitive closure DAG: Returns the transitive closure of a directed acyclic graph. for usage information, see references/transitive_closure_dag.md

Transitive reduction: Returns transitive reduction of a directed graph for usage information, see references/transitive_reduction.md

Barycenter: Calculate barycenter of a connected graph, optionally with edge weights. for usage information, see references/barycenter.md

Center: Returns the center of the graph G. for usage information, see references/center.md

Diameter: Returns the diameter of the graph G. for usage information, see references/diameter.md

Eccentricity: Returns the eccentricity of nodes in G. for usage information, see references/eccentricity.md

Effective graph resistance: Returns the Effective graph resistance of G. for usage information, see references/effective_graph_resistance.md

Harmonic diameter: Returns the harmonic diameter of the graph G. for usage information, see references/harmonic_diameter.md

Kemeny constant: Returns the Kemeny constant of the given graph. for usage information, see references/kemeny_constant.md

Periphery: Returns the periphery of the graph G. for usage information, see references/periphery.md

Radius: Returns the radius of the graph G. for usage information, see references/radius.md

Resistance distance: Returns the resistance distance between pairs of nodes in graph G. for usage information, see references/resistance_distance.md

Intersection array: Returns the intersection array of a distance-regular graph. for usage information, see references/intersection_array.md

Is distance regular: Returns True if the graph is distance regular, False otherwise. for usage information, see references/is_distance_regular.md

Is strongly regular: Returns True if and only if the given graph is strongly for usage information, see references/is_strongly_regular.md

Connected dominating set: Returns a connected dominating set. for usage information, see references/connected_dominating_set.md

Global efficiency: Returns the average global efficiency of the graph. for usage information, see references/global_efficiency.md

Local efficiency: Returns the average local efficiency of the graph. for usage information, see references/local_efficiency.md

Eulerian circuit: Returns an iterator over the edges of an Eulerian circuit in G. for usage information, see references/eulerian_circuit.md

Eulerian path: Return an iterator over the edges of an Eulerian path in G. for usage information, see references/eulerian_path.md

Eulerize: Transforms a graph into an Eulerian graph. for usage information, see references/eulerize.md

Has Eulerian path: Return True iff G has an Eulerian path. for usage information, see references/has_eulerian_path.md

Is Eulerian: Returns True if and only if G is Eulerian. for usage information, see references/is_eulerian.md

Max flow min cost: Returns a maximum (s, t)-flow of minimum cost. for usage information, see references/max_flow_min_cost.md

Min cost flow: Returns a minimum cost flow satisfying all demands in digraph G. for usage information, see references/min_cost_flow.md

Min cost flow cost: Find the cost of a minimum cost flow satisfying all demands in digraph G. for usage information, see references/min_cost_flow_cost.md

Network simplex: Find a minimum cost flow satisfying all demands in digraph G. for usage information, see references/network_simplex.md

Weisfeiler–Lehman graph hash: Return Weisfeiler Lehman (WL) graph hash. for usage information, see references/weisfeiler_lehman_graph_hash.md

Weisfeiler–Lehman subgraph hashes: Return a dictionary of subgraph hashes by node. for usage information, see references/weisfeiler_lehman_subgraph_hashes.md

Flow hierarchy: Returns the flow hierarchy of a directed network. for usage information, see references/flow_hierarchy.md

Is KL connected: Returns True if and only if G is locally (k, l)-connected. for usage information, see references/is_kl_connected.md

KL connected subgraph: Returns the maximum locally (k, l)-connected subgraph of G. for usage information, see references/kl_connected_subgraph.md

Isolates: Iterator over isolates in the graph. for usage information, see references/isolates.md

Number of isolates: Returns the number of isolates in the graph. for usage information, see references/number_of_isolates.md

Could be isomorphic: Returns False if graphs are definitely not isomorphic. for usage information, see references/could_be_isomorphic.md

Fast could be isomorphic: Returns False if graphs are definitely not isomorphic. for usage information, see references/fast_could_be_isomorphic.md

Faster could be isomorphic: Returns False if graphs are definitely not isomorphic. for usage information, see references/faster_could_be_isomorphic.md

VF2++ all isomorphisms: Yields all the possible mappings between G1 and G2. for usage information, see references/vf2pp_all_isomorphisms.md

VF2++ is isomorphic: Examines whether G1 and G2 are isomorphic. for usage information, see references/vf2pp_is_isomorphic.md

VF2++ isomorphism: Return an isomorphic mapping between G1 and G2 if it exists. for usage information, see references/vf2pp_isomorphism.md

Hits: Returns HITS hubs and authorities values for nodes. for usage information, see references/hits.md

Google matrix: Returns the Google matrix of the graph. for usage information, see references/google_matrix.md

PageRank: Returns the PageRank of the nodes in the graph. for usage information, see references/pagerank.md

Adamic–Adar index: Compute the Adamic-Adar index of all node pairs in ebunch. for usage information, see references/adamic_adar_index.md

Cn Soundarajan–Hopcroft: Count the number of common neighbors of all node pairs in ebunch for usage information, see references/cn_soundarajan_hopcroft.md

Common neighbor centrality: Return the CCPA score for each pair of nodes. for usage information, see references/common_neighbor_centrality.md

Jaccard coefficient: Compute the Jaccard coefficient of all node pairs in ebunch. for usage information, see references/jaccard_coefficient.md

Preferential attachment: Compute the preferential attachment score of all node pairs in ebunch. for usage information, see references/preferential_attachment.md

Ra index Soundarajan–Hopcroft: Compute the resource allocation index of all node pairs in for usage information, see references/ra_index_soundarajan_hopcroft.md

Resource allocation index: Compute the resource allocation index of all node pairs in ebunch. for usage information, see references/resource_allocation_index.md

Within inter cluster: Compute the ratio of within- and inter-cluster common neighbors for usage information, see references/within_inter_cluster.md

All pairs lowest common ancestor: Return the lowest common ancestor of all pairs or the provided pairs for usage information, see references/all_pairs_lowest_common_ancestor.md

Tree all pairs lowest common ancestor: Yield the lowest common ancestor for sets of pairs in a tree. for usage information, see references/tree_all_pairs_lowest_common_ancestor.md

Is matching: Return True if `matching is a valid matching of G` for usage information, see references/is_matching.md

Is maximal matching: Return True if `matching is a maximal matching of G` for usage information, see references/is_maximal_matching.md

Is perfect matching: Return True if `matching is a perfect matching for G` for usage information, see references/is_perfect_matching.md

Max weight matching: Compute a maximum-weighted matching of G. for usage information, see references/max_weight_matching.md

Maximal matching: Find a maximal matching in the graph. for usage information, see references/maximal_matching.md

Min weight matching: Compute a minimum-weight maximum-cardinality matching of G. for usage information, see references/min_weight_matching.md

Contracted nodes: Returns the graph that results from contracting u and v. for usage information, see references/contracted_nodes.md

Identified nodes: Returns the graph that results from contracting u and v. for usage information, see references/contracted_nodes.md

Moral graph: Return the Moral Graph for usage information, see references/moral_graph.md

Non randomness: Compute the non-randomness of a graph. for usage information, see references/non_randomness.md

Compose: Compose graph G with H by combining nodes and edges into a single graph. for usage information, see references/compose.md

Difference: Returns a new graph that contains the edges that exist in G but not in H. for usage information, see references/difference.md

Disjoint union: Combine graphs G and H. The nodes are assumed to be unique (disjoint). for usage information, see references/disjoint_union.md

Full join: Returns the full join of graphs G and H. for usage information, see references/full_join.md

Intersection: Returns a new graph that contains only the nodes and the edges that exist in for usage information, see references/intersection.md

Symmetric difference: Returns new graph with edges that exist in either G or H but not both. for usage information, see references/symmetric_difference.md

Union: Combine graphs G and H. The names of nodes must be unique. for usage information, see references/union.md

Cartesian product: Returns the Cartesian product of G and H. for usage information, see references/cartesian_product.md

Corona product: Returns the Corona product of G and H. for usage information, see references/corona_product.md

Lexicographic product: Returns the lexicographic product of G and H. for usage information, see references/lexicographic_product.md

Modular product: Returns the Modular product of G and H. for usage information, see references/modular_product.md

Power: Returns the specified power of a graph. for usage information, see references/power.md

Strong product: Returns the strong product of G and H. for usage information, see references/strong_product.md

Tensor product: Returns the tensor product of G and H. for usage information, see references/tensor_product.md

Complement: Returns the graph complement of G. for usage information, see references/complement.md

Reverse: Returns the reverse directed graph of G. for usage information, see references/reverse.md

Is perfect graph: Return True if G is a perfect graph, else False. for usage information, see references/is_perfect_graph.md

Check planarity: Check if a graph is planar and return a counterexample or an embedding. for usage information, see references/check_planarity.md

Is planar: Returns True if and only if G is planar. for usage information, see references/is_planar.md

Chromatic polynomial: Returns the chromatic polynomial of G for usage information, see references/chromatic_polynomial.md

Tutte polynomial: Returns the Tutte polynomial of G for usage information, see references/tutte_polynomial.md

Overall reciprocity: Compute the reciprocity for the whole graph. for usage information, see references/overall_reciprocity.md

Reciprocity: Compute the reciprocity in a directed graph. for usage information, see references/reciprocity.md

Is regular: Determines whether a graph is regular. for usage information, see references/is_regular.md

K factor: Compute a k-factor of a graph. for usage information, see references/k_factor.md

Rich club coefficient: Returns the rich-club coefficient of the graph G. for usage information, see references/rich_club_coefficient.md

Floyd–Warshall: Find all-pairs shortest path lengths using Floyd's algorithm. for usage information, see references/floyd_warshall.md

Floyd–Warshall NumPy: Find all-pairs shortest path lengths using Floyd's algorithm. for usage information, see references/floyd_warshall_numpy.md

Floyd–Warshall predecessor and distance: Find all-pairs shortest path lengths using Floyd's algorithm. for usage information, see references/floyd_warshall_predecessor_and_distance.md

All pairs all shortest paths: Compute all shortest paths between all nodes. for usage information, see references/all_pairs_all_shortest_paths.md

Average shortest path length: Returns the average shortest path length. for usage information, see references/average_shortest_path_length.md

Shortest path: Compute shortest paths in the graph. for usage information, see references/shortest_path.md

Shortest path length: Compute shortest path lengths in the graph. for usage information, see references/shortest_path_length.md

All pairs shortest path: Compute shortest paths between all nodes. for usage information, see references/all_pairs_shortest_path.md

All pairs shortest path length: Computes the shortest path lengths between all nodes in G. for usage information, see references/all_pairs_shortest_path_length.md

Bidirectional shortest path: Returns a list of nodes in a shortest path between source and target. for usage information, see references/bidirectional_shortest_path.md

Predecessor: Returns dict of predecessors for the path from source to all nodes in G. for usage information, see references/predecessor.md

Single source shortest path: Compute shortest path between source for usage information, see references/single_source_shortest_path.md

Single target shortest path: Compute shortest path to target from all nodes that reach target. for usage information, see references/single_target_shortest_path.md

All pairs Bellman–Ford path: Compute shortest paths between all nodes in a weighted graph. for usage information, see references/all_pairs_bellman_ford_path.md

All pairs Bellman–Ford path length: Compute shortest path lengths between all nodes in a weighted graph. for usage information, see references/all_pairs_bellman_ford_path_length.md

All pairs Dijkstra: Find shortest weighted paths and lengths between all nodes. for usage information, see references/all_pairs_dijkstra.md

All pairs Dijkstra path: Compute shortest paths between all nodes in a weighted graph. for usage information, see references/all_pairs_dijkstra_path.md

All pairs Dijkstra path length: Compute shortest path lengths between all nodes in a weighted graph. for usage information, see references/all_pairs_dijkstra_path_length.md

Bellman–Ford path length: Returns the shortest path length from source to target for usage information, see references/bellman_ford_path_length.md

Bellman–Ford predecessor and distance: Compute shortest path lengths and predecessors on shortest paths for usage information, see references/bellman_ford_predecessor_and_distance.md

Dijkstra path length: Returns the shortest weighted path length in G from source to target. for usage information, see references/dijkstra_path_length.md

Dijkstra predecessor and distance: Compute weighted shortest path length and predecessors. for usage information, see references/dijkstra_predecessor_and_distance.md

Find negative cycle: Returns a cycle with negative total weight if it exists. for usage information, see references/find_negative_cycle.md

Goldberg Radzik: Compute shortest path lengths and predecessors on shortest paths for usage information, see references/goldberg_radzik.md

Johnson: Uses Johnson's Algorithm to compute shortest paths. for usage information, see references/johnson.md

Multi source Dijkstra: Find shortest weighted paths and lengths from a given set of for usage information, see references/multi_source_dijkstra.md

Multi source Dijkstra path: Find shortest weighted paths in G from a given set of source for usage information, see references/multi_source_dijkstra_path.md

Multi source Dijkstra path length: Find shortest weighted path lengths in G from a given set of for usage information, see references/multi_source_dijkstra_path_length.md

Negative edge cycle: Returns True if there exists a negative edge cycle anywhere in G. for usage information, see references/negative_edge_cycle.md

Single source Bellman–Ford: Compute shortest paths and lengths in a weighted graph G. for usage information, see references/single_source_bellman_ford.md

Single source Bellman–Ford path length: Compute the shortest path length between source and all other for usage information, see references/single_source_bellman_ford_path_length.md

Single source Dijkstra: Find shortest weighted paths and lengths from a source node. for usage information, see references/single_source_dijkstra.md

Single source Dijkstra path length: Find shortest weighted path lengths in G from a source node. for usage information, see references/single_source_dijkstra_path_length.md

Generate random paths: Randomly generate sample_size paths of length path_length. for usage information, see references/generate_random_paths.md

Shortest simple paths: Returns for usage information, see references/shortest_simple_paths.md

Lattice reference: Latticize the given graph by swapping edges. for usage information, see references/lattice_reference.md

Omega: Returns the small-world coefficient (omega) of a graph for usage information, see references/omega.md

Random reference: Compute a random graph by swapping edges of a given graph. for usage information, see references/random_reference.md

Sigma: Returns the small-world coefficient (sigma) of the given graph. for usage information, see references/sigma.md

s-metric: Returns the s-metric [1]_ of graph. for usage information, see references/s_metric.md

Spanner: Returns a spanner of the given graph with the given stretch. for usage information, see references/spanner.md

Constraint: Returns the constraint on all nodes in the graph `G`. for usage information, see references/constraint.md

Effective size: Returns the effective size of all nodes in the graph `G`. for usage information, see references/effective_size.md

Dedensify: Compresses neighborhoods around high-degree nodes for usage information, see references/dedensify.md

Snap aggregation: Creates a summary graph based on attributes and connectivity. for usage information, see references/snap_aggregation.md

Connected double edge swap: Attempts the specified number of double-edge swaps in the graph G. for usage information, see references/connected_double_edge_swap.md

Directed edge swap: Swap three edges in a directed graph while keeping the node degrees fixed. for usage information, see references/directed_edge_swap.md

Double edge swap: Swap two edges in the graph while keeping the node degrees fixed. for usage information, see references/double_edge_swap.md

Is tournament: Returns True if and only if G is a tournament. for usage information, see references/is_tournament.md

BFS labeled edges: Iterate over edges in a breadth-first search (BFS) labeled by type. for usage information, see references/bfs_labeled_edges.md

BFS layers: Returns an iterator of all the layers in breadth-first search traversal. for usage information, see references/bfs_layers.md

Descendants at distance: Returns all nodes at a fixed distance from source in G. for usage information, see references/descendants_at_distance.md

Edge BFS: A directed, breadth-first-search of edges in G, beginning at source. for usage information, see references/edge_bfs.md

Edge DFS: A directed, depth-first-search of edges in G, beginning at source. for usage information, see references/edge_dfs.md

Maximum branching: Returns a maximum branching from G. for usage information, see references/maximum_branching.md

Maximum spanning arborescence: Returns a maximum spanning arborescence from G. for usage information, see references/maximum_spanning_arborescence.md

Minimum branching: Returns a minimum branching from G. for usage information, see references/minimum_branching.md

Minimum spanning arborescence: Returns a minimum spanning arborescence from G. for usage information, see references/minimum_spanning_arborescence.md

To Prüfer sequence: Returns the Prüfer sequence of the given tree. for usage information, see references/to_prufer_sequence.md

Junction tree: Returns a junction tree of a given graph. for usage information, see references/junction_tree.md

Maximum spanning edges: Generate edges in a maximum spanning forest of an undirected for usage information, see references/maximum_spanning_edges.md

Maximum spanning tree: Returns a maximum spanning tree or forest on an undirected graph G. for usage information, see references/maximum_spanning_tree.md

Minimum spanning edges: Generate edges in a minimum spanning forest of an undirected for usage information, see references/minimum_spanning_edges.md

Minimum spanning tree: Returns a minimum spanning tree or forest on an undirected graph G. for usage information, see references/minimum_spanning_tree.md

Partition spanning tree: Find a spanning tree while respecting a partition of edges. for usage information, see references/partition_spanning_tree.md

Random spanning tree: Sample a random spanning tree using the edges weights of G. for usage information, see references/random_spanning_tree.md

Is arborescence: Returns True if G is an arborescence. for usage information, see references/is_arborescence.md

Is branching: Returns True if G is a branching. for usage information, see references/is_branching.md

Is forest: Returns True if G is a forest. for usage information, see references/is_forest.md

Is tree: Returns True if G is a tree. for usage information, see references/is_tree.md

All triads: A generator of all possible triads in G. for usage information, see references/all_triads.md

Is triad: Returns True if the graph G is a triad, else False. for usage information, see references/is_triad.md

Triad type: Returns the sociological triad type for a triad. for usage information, see references/triad_type.md

Triads by type: Returns a list of all triads for each triad type in a directed graph. for usage information, see references/triads_by_type.md

Number of walks: Returns the number of walks connecting each pair of nodes in G for usage information, see references/number_of_walks.md

Gutman index: Returns the Gutman Index for the graph G. for usage information, see references/gutman_index.md

Hyper wiener index: Returns the Hyper-Wiener index of the graph G. for usage information, see references/hyper_wiener_index.md

Schultz index: Returns the Schultz Index (of the first kind) of G for usage information, see references/schultz_index.md

Wiener index: Returns the Wiener index of the given graph. for usage information, see references/wiener_index.md

Is empty: Returns True if G has no edges. for usage information, see references/is_empty.md

Is negatively weighted: Returns True if G has negatively weighted edges. for usage information, see references/is_negatively_weighted.md

Is weighted: Returns True if G has weighted edges. for usage information, see references/is_weighted.md

Set edge attributes: Sets edge attributes from a given value or dictionary of values. for usage information, see references/set_edge_attributes.md

Set node attributes: Sets node attributes from a given value or dictionary of values. for usage information, see references/set_node_attributes.md

From dict of dicts: Returns a graph from a dictionary of dictionaries. for usage information, see references/from_dict_of_dicts.md

From dict of lists: Returns a graph from a dictionary of lists. for usage information, see references/from_dict_of_lists.md

From edgelist: Returns a graph from a list of edges. for usage information, see references/from_edgelist.md

From NumPy array: Returns a graph from a 2D NumPy array. for usage information, see references/from_numpy_array.md

From Pandas adjacency: Returns a graph from Pandas DataFrame. for usage information, see references/from_pandas_adjacency.md

From Pandas edgelist: Returns a graph from Pandas DataFrame containing an edge list. for usage information, see references/from_pandas_edgelist.md

From SciPy sparse array: Creates a new graph from an adjacency matrix given as a SciPy sparse for usage information, see references/from_scipy_sparse_array.md

To NumPy array: Returns the graph adjacency matrix as a NumPy array. for usage information, see references/to_numpy_array.md

To Pandas edgelist: Returns the graph edge list as a Pandas DataFrame. for usage information, see references/to_pandas_edgelist.md

To SciPy sparse array: Returns the graph adjacency matrix as a SciPy sparse array. for usage information, see references/to_scipy_sparse_array.md

ForceAtlas2 layout: Position nodes using the ForceAtlas2 force-directed layout algorithm. for usage information, see references/forceatlas2_layout.md

Graph atlas: Returns graph number i from the Graph Atlas. for usage information, see references/graph_atlas.md

Graph atlas g: Returns the list of all graphs with up to seven nodes named in the for usage information, see references/graph_atlas_g.md

Balanced tree: Returns the perfectly balanced r-ary tree of height h. for usage information, see references/balanced_tree.md

Barbell graph: Returns the Barbell Graph: two complete graphs connected by a path. for usage information, see references/barbell_graph.md

Binomial tree: Returns the Binomial Tree of order n. for usage information, see references/binomial_tree.md

Circulant graph: Returns the circulant graph $Ci_n(x_1, x_2, ..., x_m)$ with $n$ nodes. for usage information, see references/circulant_graph.md

Circular ladder graph: Returns the circular ladder graph $CL_n$ of length n. for usage information, see references/circular_ladder_graph.md

Complete graph: Return the complete graph K_n with n nodes. for usage information, see references/complete_graph.md

Complete multipartite graph: Returns the complete multipartite graph with the specified subset sizes. for usage information, see references/complete_multipartite_graph.md

Cycle graph: Returns the cycle graph $C_n$ of cyclically connected nodes. for usage information, see references/cycle_graph.md

Dorogovtsev–Goltsev–Mendes graph: Returns the hierarchically constructed Dorogovtsev--Goltsev--Mendes graph. for usage information, see references/dorogovtsev_goltsev_mendes_graph.md

Empty graph: Returns the empty graph with n nodes and zero edges. for usage information, see references/empty_graph.md

Full r-ary tree: Creates a full r-ary tree of n nodes. for usage information, see references/full_rary_tree.md

Kneser graph: Returns the Kneser Graph with parameters n and k. for usage information, see references/kneser_graph.md

Ladder graph: Returns the Ladder graph of length n. for usage information, see references/ladder_graph.md

Lollipop graph: Returns the Lollipop Graph; `K_m connected to P_n`. for usage information, see references/lollipop_graph.md

Null graph: Returns the Null graph with no nodes or edges. for usage information, see references/null_graph.md

Path graph: Returns the Path graph P_n of linearly connected nodes. for usage information, see references/path_graph.md

Star graph: Return a star graph. for usage information, see references/star_graph.md

Tadpole graph: Returns the (m,n)-tadpole graph; `C_m connected to P_n`. for usage information, see references/tadpole_graph.md

Trivial graph: Return the Trivial graph with one node (with label 0) and no edges. for usage information, see references/trivial_graph.md

Turan graph: Return the Turan Graph for usage information, see references/turan_graph.md

Wheel graph: Return the wheel graph for usage information, see references/wheel_graph.md

Random cograph: Returns a random cograph with $2 ^ n$ nodes. for usage information, see references/random_cograph.md

Caveman graph: Returns a caveman graph of l cliques of size k. for usage information, see references/caveman_graph.md

Connected caveman graph: Returns a connected caveman graph of l cliques of size k. for usage information, see references/connected_caveman_graph.md

Gaussian random partition graph: Generate a Gaussian random partition graph. for usage information, see references/gaussian_random_partition_graph.md

LFR benchmark graph: Returns the LFR benchmark graph. for usage information, see references/LFR_benchmark_graph.md

Planted partition graph: Returns the planted l-partition graph. for usage information, see references/planted_partition_graph.md

Random partition graph: Returns the random partition graph with a partition of sizes. for usage information, see references/random_partition_graph.md

Relaxed caveman graph: Returns a relaxed caveman graph. for usage information, see references/relaxed_caveman_graph.md

Ring of cliques: Defines a "ring of cliques" graph. for usage information, see references/ring_of_cliques.md

Stochastic block model: Returns a stochastic block model graph. for usage information, see references/stochastic_block_model.md

Windmill graph: Generate a windmill graph. for usage information, see references/windmill_graph.md

Configuration model: Returns a random graph with the given degree sequence. for usage information, see references/configuration_model.md

Directed configuration model: Returns a directed_random graph with the given degree sequences. for usage information, see references/directed_configuration_model.md

Directed Havel–Hakimi graph: Returns a directed graph with the given degree sequences. for usage information, see references/directed_havel_hakimi_graph.md

Havel–Hakimi graph: Returns a simple graph with given degree sequence constructed for usage information, see references/havel_hakimi_graph.md

Random degree sequence graph: Returns a simple random graph with the given degree sequence. for usage information, see references/random_degree_sequence_graph.md

G(n,c) graph: Returns the growing network with copying (GNC) digraph with n nodes. for usage information, see references/gnc_graph.md

G(n,r) graph: Returns the growing network with redirection (GNR) digraph with n for usage information, see references/gnr_graph.md

Random k-out graph: Returns a random k-out graph with preferential attachment. for usage information, see references/random_k_out_graph.md

Scale-free graph: Returns a scale-free directed graph. for usage information, see references/scale_free_graph.md

Duplication divergence graph: Returns an undirected graph using the duplication-divergence model. for usage information, see references/duplication_divergence_graph.md

Partial duplication graph: Returns a random graph using the partial duplication model. for usage information, see references/partial_duplication_graph.md

Chordal cycle graph: Returns the chordal cycle graph on p nodes. for usage information, see references/chordal_cycle_graph.md

Is regular expander: Determines whether the graph G is a regular expander. [1]_ for usage information, see references/is_regular_expander.md

Margulis–Gabber–Galil graph: Returns the Margulis-Gabber-Galil undirected MultiGraph on n^2 nodes. for usage information, see references/margulis_gabber_galil_graph.md

Maybe regular expander graph: Utility for creating a random regular expander. for usage information, see references/maybe_regular_expander_graph.md

Paley graph: Returns the Paley $\frac{(p-1)}{2}$ -regular graph on $p$ nodes. for usage information, see references/paley_graph.md

Random regular expander graph: Returns a random regular expander graph on $n$ nodes with degree $d$. for usage information, see references/random_regular_expander_graph.md

Geometric edges: Returns edge list of node pairs within radius of each other. for usage information, see references/geometric_edges.md

Navigable small-world graph: Returns a navigable small-world graph. for usage information, see references/navigable_small_world_graph.md

Random geometric graph: Returns a random geometric graph in the unit cube of dimensions dim. for usage information, see references/random_geometric_graph.md

Soft random geometric graph: Returns a soft random geometric graph in the unit cube. for usage information, see references/soft_random_geometric_graph.md

Thresholded random geometric graph: Returns a thresholded random geometric graph in the unit cube. for usage information, see references/thresholded_random_geometric_graph.md

H(k,n) Harary graph: Return the Harary graph with given node connectivity and node number. for usage information, see references/hkn_harary_graph.md

H(n,m) Harary graph: Return the Harary graph with given numbers of nodes and edges. for usage information, see references/hnm_harary_graph.md

Random Internet as graph: Generates a random undirected graph resembling the Internet AS network for usage information, see references/random_internet_as_graph.md

General random intersection graph: Returns a random intersection graph with independent probabilities for usage information, see references/general_random_intersection_graph.md

K random intersection graph: Returns a intersection graph with randomly chosen attribute sets for for usage information, see references/k_random_intersection_graph.md

Uniform random intersection graph: Returns a uniform random intersection graph. for usage information, see references/uniform_random_intersection_graph.md

Interval graph: Generates an interval graph for a list of intervals given. for usage information, see references/interval_graph.md

Is valid directed joint degree: Checks whether the given directed joint degree input is realizable for usage information, see references/is_valid_directed_joint_degree.md

Is valid joint degree: Checks whether the given joint degree dictionary is realizable. for usage information, see references/is_valid_joint_degree.md

Joint degree graph: Generates a random simple graph with the given joint degree dictionary. for usage information, see references/joint_degree_graph.md

Grid 2D graph: Returns the two-dimensional grid graph. for usage information, see references/grid_2d_graph.md

Grid graph: Returns the n-dimensional grid graph. for usage information, see references/grid_graph.md

Hexagonal lattice graph: Returns an m by n hexagonal lattice graph. for usage information, see references/hexagonal_lattice_graph.md

Hypercube graph: Returns the n-dimensional hypercube graph. for usage information, see references/hypercube_graph.md

Triangular lattice graph: Returns the $m$ by $n$ triangular lattice graph. for usage information, see references/triangular_lattice_graph.md

Inverse line graph: Returns the inverse line graph of graph G. for usage information, see references/inverse_line_graph.md

Line graph: Returns the line graph of the graph or digraph G. for usage information, see references/line_graph.md

Mycielski graph: Generator for the n_th Mycielski Graph. for usage information, see references/mycielski_graph.md

Mycielskian: Returns the Mycielskian of a simple, undirected graph G for usage information, see references/mycielskian.md

Nonisomorphic trees: Generate nonisomorphic trees of specified order. for usage information, see references/nonisomorphic_trees.md

Number of nonisomorphic trees: Returns the number of nonisomorphic trees of the specified order. for usage information, see references/number_of_nonisomorphic_trees.md

Random clustered graph: Generate a random graph with the given joint independent edge degree and for usage information, see references/random_clustered_graph.md

Barabasi–Albert graph: Returns a random graph using Barabási–Albert preferential attachment for usage information, see references/barabasi_albert_graph.md

Binomial graph: Returns a $G_{n,p}$ random graph, also known as an Erdős-Rényi graph for usage information, see references/gnp_random_graph.md

Connected Watts–Strogatz graph: Returns a connected Watts–Strogatz small-world graph. for usage information, see references/connected_watts_strogatz_graph.md

Dense G(n,m) random graph: Returns a $G_{n,m}$ random graph. for usage information, see references/dense_gnm_random_graph.md

Dual Barabasi–Albert graph: Returns a random graph using dual Barabási–Albert preferential attachment for usage information, see references/dual_barabasi_albert_graph.md

Erdos–Renyi graph: Returns a $G_{n,p}$ random graph, also known as an Erdős-Rényi graph for usage information, see references/gnp_random_graph.md

Extended Barabasi–Albert graph: Returns an extended Barabási–Albert model graph. for usage information, see references/extended_barabasi_albert_graph.md

Fast G(n,p) random graph: Returns a $G_{n,p}$ random graph, also known as an Erdős-Rényi graph or for usage information, see references/fast_gnp_random_graph.md

G(n,m) random graph: Returns a $G_{n,m}$ random graph. for usage information, see references/gnm_random_graph.md

G(n,p) random graph: Returns a $G_{n,p}$ random graph, also known as an Erdős-Rényi graph for usage information, see references/gnp_random_graph.md

Newman–Watts–Strogatz graph: Returns a Newman–Watts–Strogatz small-world graph. for usage information, see references/newman_watts_strogatz_graph.md

Power-law cluster graph: Holme and Kim algorithm for growing graphs with powerlaw for usage information, see references/powerlaw_cluster_graph.md

Random lobster graph: Returns a random lobster graph. for usage information, see references/random_lobster_graph.md

Random power-law tree: Returns a tree with a power law degree distribution. for usage information, see references/random_powerlaw_tree.md

Random power-law tree sequence: Returns a degree sequence for a tree with a power law distribution. for usage information, see references/random_powerlaw_tree_sequence.md

Random regular graph: Returns a random $d$-regular graph on $n$ nodes. for usage information, see references/random_regular_graph.md

Random shell graph: Returns a random shell graph for the constructor given. for usage information, see references/random_shell_graph.md

Watts–Strogatz graph: Returns a Watts–Strogatz small-world graph. for usage information, see references/watts_strogatz_graph.md

Bull graph: Returns the Bull Graph for usage information, see references/bull_graph.md

Chvatal graph: Returns the Chvátal Graph for usage information, see references/chvatal_graph.md

Cubical graph: Returns the 3-regular Platonic Cubical Graph for usage information, see references/cubical_graph.md

Desargues graph: Returns the Desargues Graph for usage information, see references/desargues_graph.md

Diamond graph: Returns the Diamond graph for usage information, see references/diamond_graph.md

Dodecahedral graph: Returns the Platonic Dodecahedral graph. for usage information, see references/dodecahedral_graph.md

Frucht graph: Returns the Frucht Graph. for usage information, see references/frucht_graph.md

Generalized petersen graph: Returns the Generalized Petersen Graph GP(n,k). for usage information, see references/generalized_petersen_graph.md

Heawood graph: Returns the Heawood Graph, a (3,6) cage. for usage information, see references/heawood_graph.md

Hoffman singleton graph: Returns the Hoffman-Singleton Graph. for usage information, se