dynamo.vf.vfGraph

class dynamo.vf.vfGraph(*args, **kwds)

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.

__init__(*args, **kwds)

Initialize self. See help(type(self)) for accurate signature.

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(edges[, 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.
DictList(vertices, 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.
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([directed, index])	Reads a GraphML format file and creates a graph based on it.
Read_GraphMLz(f[, directed, 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 Pajek format file and creates a graph based on it.
Read_Pickle([fname])	Reads a graph from Python pickled format
Read_Picklez(fname, *args, **kwds)	Reads a graph from compressed Python pickled format, uncompressing it on-the-fly.
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.
TupleList(edges[, directed, …])	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.
Weighted_Adjacency(matrix[, mode, attr, loops])	Generates a graph from its weighted adjacency matrix.
__init__(*args, **kwds)	Initialize self.
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])	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])	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])	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.
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])	Calculates the (strong or weak) clusters (connected components) for a given graph.
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, return_levels])	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) clusters (connected components) for a given graph.
compose()	Returns the composition of two graphs.
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.
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.
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])	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_adjacency
eigenvector_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(g)	Converts the graph from networkx
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([path, 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_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_bipartite()	Decides whether the graph is bipartite 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()	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_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])	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_grid_fruchterman_reingold(**kwds)	Compatibility alias to the Fruchterman-Reingold layout with the grid option turned on.
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.
linegraph()	Returns the line graph 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.
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_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.
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])	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.
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([target, weights, mode])	Calculates shortest path lengths for given vertices in a graph.
shortest_paths_dijkstra([target, weights, mode])	Calculates shortest path lengths for given vertices in a graph.
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_directed()	Converts an undirected graph to directed.
to_graph_tool([graph_attributes, …])	Converts the graph to graph-tool
to_networkx()	Converts the graph to networkx format
to_prufer()	Converts a tree graph into a Prufer sequence.
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_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

es	The edge sequence of the graph
vs	The vertex sequence of the graph