Source code for networkx.algorithms.connectivity.utils

"""
Utilities for connectivity package
"""

import networkx as nx

__all__ = ["build_auxiliary_node_connectivity", "build_auxiliary_edge_connectivity"]


[docs] @nx._dispatchable(returns_graph=True) def build_auxiliary_node_connectivity(G): r"""Creates a directed graph D from an undirected graph G to compute flow based node connectivity. For an undirected graph G having `n` nodes and `m` edges we derive a directed graph D with `2n` nodes and `2m+n` arcs by replacing each original node `v` with two nodes `vA`, `vB` linked by an (internal) arc in D. Then for each edge (`u`, `v`) in G we add two arcs (`uB`, `vA`) and (`vB`, `uA`) in D. Finally we set the attribute capacity = 1 for each arc in D [1]_. For a directed graph having `n` nodes and `m` arcs we derive a directed graph D with `2n` nodes and `m+n` arcs by replacing each original node `v` with two nodes `vA`, `vB` linked by an (internal) arc (`vA`, `vB`) in D. Then for each arc (`u`, `v`) in G we add one arc (`uB`, `vA`) in D. Finally we set the attribute capacity = 1 for each arc in D. A dictionary with a mapping between nodes in the original graph and the auxiliary digraph is stored as a graph attribute: D.graph['mapping']. References ---------- .. [1] Kammer, Frank and Hanjo Taubig. Graph Connectivity. in Brandes and Erlebach, 'Network Analysis: Methodological Foundations', Lecture Notes in Computer Science, Volume 3418, Springer-Verlag, 2005. https://doi.org/10.1007/978-3-540-31955-9_7 """ directed = G.is_directed() mapping = {} H = nx.DiGraph() for i, node in enumerate(G): mapping[node] = i H.add_node(f"{i}A", id=node) H.add_node(f"{i}B", id=node) H.add_edge(f"{i}A", f"{i}B", capacity=1) edges = [] for source, target in G.edges(): edges.append((f"{mapping[source]}B", f"{mapping[target]}A")) if not directed: edges.append((f"{mapping[target]}B", f"{mapping[source]}A")) H.add_edges_from(edges, capacity=1) # Store mapping as graph attribute H.graph["mapping"] = mapping return H
[docs] @nx._dispatchable(returns_graph=True) def build_auxiliary_edge_connectivity(G): """Auxiliary digraph for computing flow based edge connectivity If the input graph is undirected, we replace each edge (`u`,`v`) with two reciprocal arcs (`u`, `v`) and (`v`, `u`) and then we set the attribute 'capacity' for each arc to 1. If the input graph is directed we simply add the 'capacity' attribute. Part of algorithm 1 in [1]_ . References ---------- .. [1] Abdol-Hossein Esfahanian. Connectivity Algorithms. (this is a chapter, look for the reference of the book). http://www.cse.msu.edu/~cse835/Papers/Graph_connectivity_revised.pdf """ if G.is_directed(): H = nx.DiGraph() H.add_nodes_from(G.nodes()) H.add_edges_from(G.edges(), capacity=1) return H else: H = nx.DiGraph() H.add_nodes_from(G.nodes()) for source, target in G.edges(): H.add_edges_from([(source, target), (target, source)], capacity=1) return H