dynamo.vf.vfGraph
- class dynamo.vf.vfGraph(*args, **kwds)[source]
A class for manipulating the graph creating from the transition matrix, built from the (reconstructed) vector field. This is a derived class from igraph’s Graph.
Methods
Adjacency(matrix[, mode])Generates a graph from its adjacency matrix.
Asymmetric_Preference(type_dist_matrix, ...)Generates a graph based on asymmetric vertex types and connection probabilities.
Atlas()Generates a graph from the Graph Atlas.
Barabasi(m[, outpref, directed, power, ...])Generates a graph based on the Barabasi-Albert model.
Bipartite(types, edges[, directed])Creates a bipartite graph with the given vertex types and edges.
DataFrame([directed, vertices, use_vids])Generates a graph from one or two dataframes.
De_Bruijn(n)Generates a de Bruijn graph with parameters (m, n)
Degree_Sequence([in_, method])Generates a graph with a given degree sequence.
DictDict([directed, vertex_name_attr])Constructs a graph from a dict-of-dicts representation.
DictList(edges[, directed, ...])Constructs a graph from a list-of-dictionaries representation.
Erdos_Renyi(p, m[, directed, loops])Generates a graph based on the Erdos-Renyi model.
Establishment(k, type_dist, pref_matrix[, ...])Generates a graph based on a simple growing model with vertex types.
Famous()Generates a famous graph based on its name.
Forest_Fire(fw_prob[, bw_factor, ambs, directed])Generates a graph based on the forest fire model
Formula([formula, attr, simplify])Generates a graph from a graph formula
Full([directed, loops])Generates a full graph (directed or undirected, with or without loops).
Full_Bipartite(n1, n2[, directed, mode])Generates a full bipartite graph (directed or undirected, with or without loops).
Full_Citation([directed])Generates a full citation graph
GRG(n, radius[, torus])Generates a random geometric graph.
Growing_Random(m[, directed, citation])Generates a growing random graph.
Incidence(matrix[, directed, mode, ...])Creates a bipartite graph from an incidence matrix.
Isoclass(cls[, directed])Generates a graph with a given isomorphism class.
K_Regular(k[, directed, multiple])Generates a k-regular random graph
Kautz(n)Generates a Kautz graph with parameters (m, n)
LCF(shifts, repeats)Generates a graph from LCF notation.
Lattice([nei, directed, mutual, circular])Generates a regular lattice.
ListDict([directed, vertex_name_attr])Constructs a graph from a dict-of-lists representation.
Load(f[, format])Unified reading function for graphs.
Preference(type_dist, pref_matrix[, ...])Generates a graph based on vertex types and connection probabilities.
Random_Bipartite(n1, n2[, p, m, directed, ...])Generates a random bipartite graph with the given number of vertices and edges (if m is given), or with the given number of vertices and the given connection probability (if p is given).
Read(f[, format])Unified reading function for graphs.
Read_Adjacency(f[, sep, comment_char, attribute])Constructs a graph based on an adjacency matrix from the given file.
Read_DIMACS(f[, directed])Reads a graph from a file conforming to the DIMACS minimum-cost flow file format.
Read_DL([directed])Reads an UCINET DL file and creates a graph based on it.
Read_Edgelist([directed])Reads an edge list from a file and creates a graph based on it.
Read_GML()Reads a GML file and creates a graph based on it.
Read_GraphDB([directed])Reads a GraphDB format file and creates a graph based on it.
Read_GraphML([index])Reads a GraphML format file and creates a graph based on it.
Read_GraphMLz(f[, index])Reads a graph from a zipped GraphML file.
Read_Lgl([names, weights, directed])Reads an .lgl file used by LGL.
Read_Ncol([names, weights, directed])Reads an .ncol file used by LGL.
Read_Pajek()Reads a file in the Pajek format and creates a graph based on it.
Read_Pickle([fname])Reads a graph from Python pickled format
Read_Picklez(fname)Reads a graph from compressed Python pickled format, uncompressing it on-the-fly.
Realize_Degree_Sequence([in_, ...])Generates a graph from a degree sequence.
Recent_Degree(m, window[, outpref, ...])Generates a graph based on a stochastic model where the probability of an edge gaining a new node is proportional to the edges gained in a given time window.
Ring([directed, mutual, circular])Generates a ring graph.
SBM(pref_matrix, block_sizes[, directed, loops])Generates a graph based on a stochastic blockmodel.
Star([mode, center])Generates a star graph.
Static_Fitness(fitness_out[, fitness_in, ...])Generates a non-growing graph with edge probabilities proportional to node fitnesses.
Static_Power_Law(m, exponent_out[, ...])Generates a non-growing graph with prescribed power-law degree distributions.
Tree(children[, type])Generates a tree in which almost all vertices have the same number of children.
Tree_Game([directed, method])Generates a random tree by sampling uniformly from the set of labelled trees with a given number of nodes.
TupleList([directed, vertex_name_attr, ...])Constructs a graph from a list-of-tuples representation.
Watts_Strogatz(size, nei, p[, loops, multiple])@param dim: the dimension of the lattice @param size: the size of the lattice along all dimensions @param nei: value giving the distance (number of steps) within which two vertices will be connected. @param p: rewiring probability.
Weighted_Adjacency(matrix[, mode, attr, loops])Generates a graph from its weighted adjacency matrix.
__init__(*args, **kwds)add_edge(source, target, **kwds)Adds a single edge to the graph.
add_edges(es[, attributes])Adds some edges to the graph.
add_vertex([name])Adds a single vertex to the graph.
add_vertices(n[, attributes])Adds some vertices to the graph.
adhesion([target, checks])Calculates the edge connectivity of the graph or between some vertices.
all_minimal_st_separators()Returns a list containing all the minimal s-t separators of a graph.
all_st_cuts(source, target)Returns all the cuts between the source and target vertices in a directed graph.
all_st_mincuts(source, target[, capacity])Returns all the mincuts between the source and target vertices in a directed graph.
alpha()Returns the independence number of the graph.
are_connected(v2)Decides whether two given vertices are directly connected.
articulation_points()Returns the list of articulation points in the graph.
as_directed(*args, **kwds)Returns a directed copy of this graph.
as_undirected(*args, **kwds)Returns an undirected copy of this graph.
assortativity([types2, directed, normalized])Returns the assortativity of the graph based on numeric properties of the vertices.
assortativity_degree()Returns the assortativity of a graph based on vertex degrees.
assortativity_nominal([directed, normalized])Returns the assortativity of the graph based on vertex categories.
attributes()@return: the attribute name list of the graph
authority_score([scale, arpack_options, ...])Calculates Kleinberg's authority score for the vertices of the graph
average_path_length([unconn, weights])Calculates the average path length in a graph.
betweenness([directed, cutoff, weights, ...])Calculates or estimates the betweenness of vertices in a graph.
bfs([mode])Conducts a breadth first search (BFS) on the graph.
bfsiter([mode, advanced])Constructs a breadth first search (BFS) iterator of the graph.
bibcoupling()Calculates bibliographic coupling scores for given vertices in a graph.
biconnected_components([...])Calculates the biconnected components of the graph.
bipartite_projection([types, multiplicity, ...])Projects a bipartite graph into two one-mode graphs.
bipartite_projection_size([types])Calculates the number of vertices and edges in the bipartite projections of this graph according to the specified vertex types.
blocks([return_articulation_points])Calculates the biconnected components of the graph.
bridges()Returns the list of bridges in the graph.
build_graph(adj_mat)build sparse diffusion graph.
canonical_permutation([color])Calculates the canonical permutation of a graph using the BLISS isomorphism algorithm.
chordal_completion([alpham1])Returns the list of edges needed to be added to the graph to make it chordal.
clear()Clears the graph, deleting all vertices, edges, and attributes.
clique_number()Returns the clique number of the graph.
cliques([max])Returns some or all cliques of the graph as a list of tuples.
closeness([mode, cutoff, weights, normalized])Calculates the closeness centralities of given vertices in a graph.
clusters([mode])Deprecated alias to L{Graph.connected_components()}.
cocitation()Calculates cocitation scores for given vertices in a graph.
cohesion([target, checks, neighbors])Calculates the vertex connectivity of the graph or between some vertices.
cohesive_blocks()Calculates the cohesive block structure of the graph.
community_edge_betweenness([clusters, ...])Community structure based on the betweenness of the edges in the network.
community_fastgreedy([weights])Community structure based on the greedy optimization of modularity.
community_infomap([edge_weights, ...])Finds the community structure of the network according to the Infomap method of Martin Rosvall and Carl T.
community_label_propagation([weights, ...])Finds the community structure of the graph according to the label propagation method of Raghavan et al.
community_leading_eigenvector([clusters, ...])Newman's leading eigenvector method for detecting community structure.
community_leading_eigenvector_naive([...])Naive implementation of Newman's eigenvector community structure detection.
community_leiden([objective_function, ...])Finds the community structure of the graph using the Leiden algorithm of Traag, van Eck & Waltman.
community_multilevel([weights, ...])Community structure based on the multilevel algorithm of Blondel et al.
community_optimal_modularity(*args, **kwds)Calculates the optimal modularity score of the graph and the corresponding community structure.
community_spinglass(*args, **kwds)Finds the community structure of the graph according to the spinglass community detection method of Reichardt & Bornholdt.
community_walktrap([weights, steps])Community detection algorithm of Latapy & Pons, based on random walks.
complementer()Returns the complementer of the graph
components([mode])Calculates the (strong or weak) connected components for a given graph.
compose()Returns the composition of two graphs.
connected_components([mode])Calculates the (strong or weak) connected components for a given graph.
constraint([weights])Calculates Burt's constraint scores for given vertices in a graph.
contract_vertices([combine_attrs])Contracts some vertices in the graph, i.e. replaces groups of vertices with single vertices.
convergence_degree()Undocumented (yet).
convergence_field_size()Undocumented (yet).
convert_to_class(obj)copy()Creates a copy of the graph.
coreness()Finds the coreness (shell index) of the vertices of the network.
count_automorphisms_vf2([color, edge_color, ...])Returns the number of automorphisms of the graph.
count_isomorphisms_vf2([color1, color2, ...])Determines the number of isomorphisms between the graph and another one
count_multiple()Counts the multiplicities of the given edges.
count_subisomorphisms_vf2([color1, color2, ...])Determines the number of subisomorphisms between the graph and another one
cut_vertices()Returns the list of articulation points in the graph.
decompose([maxcompno, minelements])Decomposes the graph into subgraphs.
degree([mode, loops])Returns some vertex degrees from the graph.
degree_distribution([bin_width])Calculates the degree distribution of the graph.
delete_edges(*args, **kwds)Deletes some edges from the graph.
delete_vertices()Deletes vertices and all its edges from the graph.
density()Calculates the density of the graph.
dfs(vid[, mode])Conducts a depth first search (DFS) on the graph.
dfsiter([mode, advanced])Constructs a depth first search (DFS) iterator of the graph.
diameter([unconn, weights])Calculates the diameter of the graph.
difference()Subtracts the given graph from the original
disjoint_union(other)Creates the disjoint union of two (or more) graphs.
distances([target, weights, mode])Calculates shortest path lengths for given vertices in a graph.
diversity([weights])Calculates the structural diversity index of the vertices.
dominator([mode])Returns the dominator tree from the given root node
dyad_census(*args, **kwds)Calculates the dyad census of the graph.
eccentricity([mode])Calculates the eccentricities of given vertices in a graph.
ecount()Counts the number of edges.
edge_attributes()@return: the attribute name list of the edges of the graph
edge_betweenness([cutoff, weights, sources, ...])Calculates or estimates the edge betweennesses in a graph.
edge_connectivity([target, checks])Calculates the edge connectivity of the graph or between some vertices.
edge_disjoint_paths([target, checks])Calculates the edge connectivity of the graph or between some vertices.
eigen_adjacencyeigenvector_centrality([scale, weights, ...])Calculates the eigenvector centralities of the vertices in a graph.
evcent([scale, weights, return_eigenvalue, ...])Calculates the eigenvector centralities of the vertices in a graph.
farthest_points([unconn, weights])Returns two vertex IDs whose distance equals the actual diameter of the graph.
feedback_arc_set([method])Calculates an approximately or exactly minimal feedback arc set.
from_graph_tool(g)Converts the graph from graph-tool
from_networkx([vertex_attr_hashable])Converts the graph from networkx
fundamental_cycles([cutoff])Finds a single fundamental cycle basis of the graph
get_adjacency([type, attribute, default, eids])Returns the adjacency matrix of a graph.
get_adjacency_sparse([attribute])Returns the adjacency matrix of a graph as a SciPy CSR matrix.
get_adjlist([mode])Returns the adjacency list representation of the graph.
get_all_shortest_paths([to, weights, mode])Calculates all of the shortest paths from/to a given node in a graph.
get_all_simple_paths(v[, to, cutoff, mode])Calculates all the simple paths from a given node to some other nodes (or all of them) in a graph.
get_automorphisms_vf2([color, edge_color, ...])Returns all the automorphisms of the graph
get_diameter([unconn, weights])Returns a path with the actual diameter of the graph.
get_edge_dataframe()Export edges with attributes to pandas.DataFrame
get_edgelist()Returns the edge list of a graph.
get_eid(v2[, directed, error])Returns the edge ID of an arbitrary edge between vertices v1 and v2
get_eids([directed, error])Returns the edge IDs of some edges between some vertices.
get_incidence([types])Returns the incidence matrix of a bipartite graph.
get_inclist([mode])Returns the incidence list representation of the graph.
get_isomorphisms_vf2([color1, color2, ...])Returns all isomorphisms between the graph and another one
get_k_shortest_paths(to[, k, weights, mode, ...])Calculates the k shortest paths from/to a given node in a graph.
get_shortest_paths([to, weights, mode, output])Calculates the shortest paths from/to a given node in a graph.
get_subisomorphisms_lad([domains, induced, ...])Returns all subisomorphisms between the graph and another one using the LAD algorithm.
get_subisomorphisms_vf2([color1, color2, ...])Returns all subisomorphisms between the graph and another one
get_vertex_dataframe()Export vertices with attributes to pandas.DataFrame
girth()Returns the girth of the graph.
gomory_hu_tree([capacity, flow])Calculates the Gomory-Hu tree of an undirected graph with optional edge capacities.
harmonic_centrality([mode, cutoff, weights, ...])Calculates the harmonic centralities of given vertices in a graph.
has_multiple()Checks whether the graph has multiple edges.
hub_score([scale, arpack_options, ...])Calculates Kleinberg's hub score for the vertices of the graph
incident([mode])Returns the edges a given vertex is incident on.
indegree(*args, **kwds)Returns the in-degrees in a list.
independence_number()Returns the independence number of the graph.
independent_vertex_sets([max])Returns some or all independent vertex sets of the graph as a list of tuples.
induced_subgraph([implementation])Returns a subgraph spanned by the given vertices.
intersection(other[, byname])Creates the intersection of two (or more) graphs.
is_acyclic()Returns whether the graph is acyclic (i.e.
is_bipartite()Decides whether the graph is bipartite or not.
is_chordal([alpham1])Returns whether the graph is chordal or not.
is_connected()Decides whether the graph is connected.
is_dag()Checks whether the graph is a DAG (directed acyclic graph).
is_directed()Checks whether the graph is directed.
is_loop()Checks whether a specific set of edges contain loop edges
is_minimal_separator()Decides whether the given vertex set is a minimal separator.
is_multiple()Checks whether an edge is a multiple edge.
is_mutual([loops])Checks whether an edge has an opposite pair.
is_named()Returns whether the graph is named.
is_separator()Decides whether the removal of the given vertices disconnects the graph.
is_simple()Checks whether the graph is simple (no loop or multiple edges).
is_tree()Checks whether the graph is a (directed or undirected) tree graph.
is_weighted()Returns whether the graph is weighted.
isoclass()Returns the isomorphism class of the graph or its subgraph.
isomorphic()Checks whether the graph is isomorphic to another graph.
isomorphic_bliss([return_mapping_12, ...])Checks whether the graph is isomorphic to another graph, using the BLISS isomorphism algorithm.
isomorphic_vf2([color1, color2, ...])Checks whether the graph is isomorphic to another graph, using the VF2 isomorphism algorithm.
k_core(*args)Returns some k-cores of the graph.
knn([weights])Calculates the average degree of the neighbors for each vertex, and the same quantity as the function of vertex degree.
laplacian([normalized, mode])Returns the Laplacian matrix of a graph.
largest_cliques()Returns the largest cliques of the graph as a list of tuples.
largest_independent_vertex_sets()Returns the largest independent vertex sets of the graph as a list of tuples.
layout([layout])Returns the layout of the graph according to a layout algorithm.
layout_auto(*args, **kwds)Chooses and runs a suitable layout function based on simple topological properties of the graph.
layout_bipartite(**kwds)Place the vertices of a bipartite graph in two layers.
layout_circle(**kwds)Places the vertices of the graph uniformly on a circle or a sphere.
layout_davidson_harel(**kwds)Places the vertices on a 2D plane according to the Davidson-Harel layout algorithm.
layout_drl(**kwds)Places the vertices on a 2D plane or in the 3D space ccording to the DrL layout algorithm.
layout_fruchterman_reingold(**kwds)Places the vertices on a 2D plane according to the Fruchterman-Reingold algorithm.
layout_fruchterman_reingold_3d(**kwds)Alias for L{layout_fruchterman_reingold()} with dim=3.
layout_graphopt(**kwds)This is a port of the graphopt layout algorithm by Michael Schmuhl.
layout_grid(**kwds)Places the vertices of a graph in a 2D or 3D grid.
layout_grid_3d(**kwds)Alias for L{layout_grid()} with dim=3.
layout_kamada_kawai(**kwds)Places the vertices on a plane according to the Kamada-Kawai algorithm.
layout_kamada_kawai_3d(**kwds)Alias for L{layout_kamada_kawai()} with dim=3.
layout_lgl(**kwds)Places the vertices on a 2D plane according to the Large Graph Layout.
layout_mds(**kwds)Places the vertices in an Euclidean space with the given number of dimensions using multidimensional scaling.
layout_random(**kwds)Places the vertices of the graph randomly.
layout_random_3d(**kwds)Alias for L{layout_random()} with dim=3.
layout_reingold_tilford(**kwds)Places the vertices on a 2D plane according to the Reingold-Tilford layout algorithm.
layout_reingold_tilford_circular(**kwds)Circular Reingold-Tilford layout for trees.
layout_sphere(**kwds)Alias for L{layout_circle()} with dim=3.
layout_star(**kwds)Calculates a star-like layout for the graph.
layout_sugiyama([layers, weights, hgap, ...])Places the vertices using a layered Sugiyama layout.
layout_umap(**kwds)Uniform Manifold Approximation and Projection (UMAP).
linegraph()Returns the line graph of the graph.
list_triangles()Lists the triangles of the graph
maxdegree([mode, loops])Returns the maximum degree of a vertex set in the graph.
maxflow(source, target[, capacity])Returns a maximum flow between the given source and target vertices in a graph.
maxflow_value(target[, capacity])Returns the value of the maximum flow between the source and target vertices.
maximal_cliques([max, file])Returns the maximal cliques of the graph as a list of tuples.
maximal_independent_vertex_sets()Returns the maximal independent vertex sets of the graph as a list of tuples.
maximum_bipartite_matching([types, weights, eps])Finds a maximum matching in a bipartite graph.
maximum_cardinality_search()Conducts a maximum cardinality search on the graph.
mincut([source, target, capacity])Calculates the minimum cut between the given source and target vertices or within the whole graph.
mincut_value([target, capacity])Returns the minimum cut between the source and target vertices or within the whole graph.
minimum_cycle_basis([complete, use_cycle_order])Computes a minimum cycle basis of the graph
minimum_size_separators()Returns a list containing all separator vertex sets of minimum size.
modularity(membership[, weights, ...])Calculates the modularity score of the graph with respect to a given clustering.
modularity_matrix([resolution, directed])Calculates the modularity matrix of the graph.
motifs_randesu([cut_prob, callback])Counts the number of motifs in the graph
motifs_randesu_estimate([cut_prob, sample])Counts the total number of motifs in the graph
motifs_randesu_no([cut_prob])Counts the total number of motifs in the graph
multimaxflow(sources, sinks)Multi-source multi-sink maximum flow.
neighborhood([order, mode, mindist])For each vertex specified by I{vertices}, returns the vertices reachable from that vertex in at most I{order} steps.
neighborhood_size([order, mode, mindist])For each vertex specified by I{vertices}, returns the number of vertices reachable from that vertex in at most I{order} steps.
neighbors([mode])Returns adjacent vertices to a given vertex.
omega()Returns the clique number of the graph.
outdegree(*args, **kwds)Returns the out-degrees in a list.
pagerank([vertices, directed, damping, ...])Calculates the PageRank values of a graph.
path_length_hist([directed])Returns the path length histogram of the graph
permute_vertices()Permutes the vertices of the graph according to the given permutation and returns the new graph.
personalized_pagerank([directed, damping, ...])Calculates the personalized PageRank values of a graph.
predecessors()Returns the predecessors of a given vertex.
radius()Calculates the radius of the graph.
random_walk(steps[, mode, stuck, weights, ...])Performs a random walk of a given length from a given node.
reciprocity([mode])Reciprocity defines the proportion of mutual connections in a directed graph.
reverse_edges()Reverses the direction of some edges in the graph.
rewire([mode])Randomly rewires the graph while preserving the degree distribution.
rewire_edges([loops, multiple])Rewires the edges of a graph with constant probability.
save(f[, format])Unified writing function for graphs.
shell_index()Finds the coreness (shell index) of the vertices of the network.
shortest_paths(*args, **kwds)Deprecated alias to L{Graph.distances()}.
shortest_paths_dijkstra(*args, **kwds)Deprecated alias to L{Graph.distances()}.
similarity_dice([pairs, mode, loops])Dice similarity coefficient of vertices.
similarity_inverse_log_weighted([mode])Inverse log-weighted similarity coefficient of vertices.
similarity_jaccard([pairs, mode, loops])Jaccard similarity coefficient of vertices.
simplify([loops, combine_edges])Simplifies a graph by removing self-loops and/or multiple edges.
spanning_tree([weights, return_tree])Calculates a minimum spanning tree for a graph.
st_mincut(source, target[, capacity])Calculates the minimum cut between the source and target vertices in a graph.
strength([mode, loops, weights])Returns the strength (weighted degree) of some vertices from the graph
subcomponent([mode])Determines the indices of vertices which are in the same component as a given vertex.
subgraph([implementation])Returns a subgraph spanned by the given vertices.
subgraph_edges([delete_vertices])Returns a subgraph spanned by the given edges.
subisomorphic_lad([domains, induced, ...])Checks whether a subgraph of the graph is isomorphic to another graph.
subisomorphic_vf2([color1, color2, ...])Checks whether a subgraph of the graph is isomorphic to another graph.
successors()Returns the successors of a given vertex.
summary([verbosity, width])Returns the summary of the graph.
to_dict_dict([use_vids, edge_attrs, ...])Export graph to dictionary of dicts of edge attributes
to_dict_list([use_vids, skip_none, ...])Export graph as two lists of dictionaries, for vertices and edges.
to_directed()Converts an undirected graph to directed.
to_graph_tool([graph_attributes, ...])Converts the graph to graph-tool
to_list_dict([use_vids, ...])Export graph to a dictionary of lists (or other sequences).
to_networkx([create_using, vertex_attr_hashable])Converts the graph to networkx format.
to_prufer()Converts a tree graph into a Prufer sequence.
to_tuple_list([use_vids, edge_attrs, ...])Export graph to a list of edge tuples
to_undirected([combine_edges])Converts a directed graph to undirected.
topological_sorting()Calculates a possible topological sorting of the graph.
transitivity_avglocal_undirected([mode, weights])Calculates the average of the vertex transitivities of the graph.
transitivity_local_undirected([mode, weights])Calculates the local transitivity (clustering coefficient) of the given vertices in the graph.
transitivity_undirected()Calculates the global transitivity (clustering coefficient) of the graph.
triad_census(*args, **kwds)Calculates the triad census of the graph.
unfold_tree([mode])Unfolds the graph using a BFS to a tree by duplicating vertices as necessary.
union(other[, byname])Creates the union of two (or more) graphs.
vcount()Counts the number of vertices.
vertex_attributes()@return: the attribute name list of the vertices of the graph
vertex_coloring_greedy()Calculates a greedy vertex coloring for the graph based on some heuristics.
vertex_connectivity([target, checks, neighbors])Calculates the vertex connectivity of the graph or between some vertices.
vertex_disjoint_paths([target, checks, ...])Calculates the vertex connectivity of the graph or between some vertices.
write(f[, format])Unified writing function for graphs.
write_adjacency(f[, sep, eol])Writes the adjacency matrix of the graph to the given file
write_dimacs(f[, source, target, capacity])Writes the graph in DIMACS format to the given file.
write_dot()Writes the graph in DOT format to the given file.
write_edgelist()Writes the edge list of a graph to a file.
write_gml([creator, ids])Writes the graph in GML format to the given file.
write_graphml()Writes the graph to a GraphML file.
write_graphmlz(f[, compresslevel])Writes the graph to a zipped GraphML file.
write_leda([names, weights])Writes the graph to a file in LEDA native format.
write_lgl([names, weights, isolates])Writes the edge list of a graph to a file in .lgl format.
write_ncol([names, weights])Writes the edge list of a graph to a file in .ncol format.
write_pajek()Writes the graph in Pajek format to the given file.
write_pickle([fname, version])Saves the graph in Python pickled format
write_picklez([fname, version])Saves the graph in Python pickled format, compressed with gzip.
write_svg(fname[, layout, width, height, ...])Saves the graph as an SVG (Scalable Vector Graphics) file
Attributes
esThe edge sequence of the graph
vsThe vertex sequence of the graph