You can rate examples to help us improve the quality of examples. Weakly Connected Component A weakly connected component is a maximal subgraph of a directed graph such that for every pair of vertices, in the subgraph, there is an undirected path from to and a directed path from to. wcc = weakly_connected_components (G) graph_list = [] for c in wcc: graph_list. @not_implemented_for ('undirected') def weakly_connected_components (G): """Generate weakly connected components of G. Parameters-----G : NetworkX graph A directed graph Returns-----comp : generator of sets A generator of sets of nodes, one for each weakly connected component of G. Raises-----NetworkXNotImplemented: If G is undirected. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. comp – Generate weakly connected components of G. Generate a sorted list of weakly connected components, largest first. Graph Theory and NetworkX - Part 2: Connectivity and Distance 5 minute read In the third post in this series, we will be introducing the concept of network centrality, which introduces measures of importance for network components.In order to prepare for this, in this post, we will be looking at network connectivity and at how to measure distances or path lengths in a graph. Source code for networkx.algorithms.components.weakly_connected. If you only want the largest component, it’s more efficient to Graph, node, and edge attributes are copied to the subgraphs. """ The following are 21 code examples for showing how to use networkx.is_weakly_connected().These examples are extracted from open source projects. Generate a sorted list of weakly connected components, largest first. 我们从Python开源项目中，提取了以下6个代码示例，用于说明如何使用networkx.weakly_connected_component_subgraphs()。 is_weakly_connected¶ is_weakly_connected (G) [source] ¶. Parameters: G (NetworkX Graph) – An undirected graph. >>> G = nx.path_graph(4, create_using=nx.DiGraph()) >>> G.add_path([10, 11, 12]) >>> [len(c) for c in sorted(nx.weakly_connected_component_subgraphs(G),... key=len, reverse=True)] [4, 3] If you only want the largest component, it’s more efficient to use max instead of sort. is_weakly_connected (G) Test directed graph for weak connectivity. These are the top rated real world Python examples of networkx.weakly_connected_components extracted from open source projects. def weakly_connected_component_subgraphs (G): """Return weakly connected components as subgraphs. connected_components() Notes. And we can implement .strongly_connected_components(G) and strongly_connected_subgraphs to verify. networkx.algorithms.components.number_weakly_connected_components¶ number_weakly_connected_components (G) [source] ¶. copy (bool (default=True)) – If True make a copy of the graph attributes; Returns: comp – A generator of graphs, one for each weakly connected component of G. Return type: generator. copy (bool (default=True)) – If True make a copy of the graph attributes; Returns: comp – A generator of graphs, one for each weakly connected component of G. Return type: generator. Parameters: G (NetworkX graph) – An undirected graph. copy (bool (default=True)) – If True make a copy of the graph attributes; Returns: comp – A generator of graphs, one for each weakly connected component of G. Return type: generator In mathematics and computer science, connectivity is one of the basic concepts of graph theory: it asks for the minimum number of elements (nodes or edges) that need to be removed to separate the remaining nodes into isolated subgraphs. G (NetworkX graph) – A directed graph. © Copyright 2015, NetworkX Developers. Python networkx 模块， weakly_connected_component_subgraphs() 实例源码. A generator of graphs, one for each weakly connected component of G. Generate a sorted list of weakly connected components, largest first. # -*- coding: utf-8 -*-"""Weakly connected components.""" Returns: n – Number of weakly connected components: Return type: integer: See also. A directed graph is weakly connected if, and only if, the graph is connected when the direction of the edge between nodes is ignored. G (NetworkX graph) – A directed graph. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. Return the number of weakly connected components in G. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. This documents an unmaintained version of NetworkX. Parameters : G: NetworkX … G (NetworkX graph) – A directed graph. Raises: NetworkXNotImplemented: – If G is undirected. ... Test directed graph for weak connectivity. strongly_connected_components(), connected_components(). If you only want the largest component, it’s more efficient to Generate weakly connected components as subgraphs. Returns: connected – True if the graph is weakly connected… G (NetworkX graph) – A directed graph. Networkx allows us to find paths between nodes easily in a Graph. Test directed graph for weak connectivity. networkx.algorithms.components.weakly_connected.number_weakly_connected_components¶ number_weakly_connected_components (G) [source] ¶. Adding attributes to graphs, nodes, and edges, Converting to and from other data formats. Parameters: G (NetworkX graph) – A directed graph: Returns: comp – A generator of sets of nodes, one for each weakly connected component of G.: Return type: generator of sets Adding attributes to graphs, nodes, and edges, Converting to and from other data formats. The following are 10 code examples for showing how to use networkx.number_weakly_connected_components().These examples are extracted from open source projects. Parameters: G (NetworkX Graph) – A directed graph. The connectivity of a graph is an important measure of its resilience as a network. 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