is_weakly_connected¶ is_weakly_connected (G) [source] ¶. As I understand connected_components () method in NetworkX should generate components in a given undirected graph (There are strongly_connected_components () and weakly_connected_components () for directed graph). Python networkx 模块， weakly_connected_component_subgraphs() 实例源码. Parameters: G (NetworkX graph) – An undirected graph: Returns: comp – A generator of sets of nodes, one for each component of G.: Return type: generator of sets: Raises: NetworkXNotImplemented: – … Parameters : G: NetworkX … It is closely related to the theory of network flow problems. Source code for networkx.algorithms.components.weakly_connected. 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. : Returns: connected – True if the graph is connected, false otherwise. If you only want the largest component, it’s more efficient to # -*- coding: utf-8 -*-"""Weakly connected components.""" ... Test directed graph for weak connectivity. The following are 23 code examples for showing how to use networkx.weakly_connected_component_subgraphs().These examples are extracted from open source projects. Parameters: G (NetworkX Graph) – A directed graph. Raises: NetworkXNotImplemented: – If G is undirected. Please upgrade to a maintained version and see the current NetworkX documentation. This documents an unmaintained version of NetworkX. For directed graphs only. 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. Components » is_weakly_connected; Warning. 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 Returns: n – Number of weakly connected components: Return type: integer: See also. connected_components() Notes. Networkx allows us to find paths between nodes easily in a 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. 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. G (NetworkX graph) – A directed graph. is_weakly_connected (G) Test directed graph for weak connectivity. wcc = weakly_connected_components (G) graph_list = [] for c in wcc: graph_list. 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 def weakly_connected_component_subgraphs (G): """Return weakly connected components as subgraphs. Adding attributes to graphs, nodes, and edges, Converting to and from other data formats. You can rate examples to help us improve the quality of examples. The connectivity of a graph is an important measure of its resilience as a network. If you only want the largest component, it’s more efficient to comp – G (NetworkX graph) – A directed graph. Generate weakly connected components as subgraphs. append (G. subgraph (c). Raises: NetworkXNotImplemented: – If G is undirected. 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()。 This documents an unmaintained version of NetworkX. @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. And we can implement .strongly_connected_components(G) and strongly_connected_subgraphs to verify. Returns-----comp : generator of lists A list of graphs, one for each strongly connected component of G. copy : boolean if copy is True, Graph, node, and edge attributes are copied to the subgraphs. G (NetworkX graph) – A directed graph. 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. This documents an unmaintained version of NetworkX. ; copy (bool (default=True)) – If True make a copy of the graph attributes; Returns: comp – A generator of graphs, one for each connected component of G.. Return type: generator. copy ()) return graph_list Let us closely examine the following Graph: Raises: NetworkXNotImplemented: – If G is undirected. G (NetworkX graph) – A directed graph. G (NetworkX graph) – A directed graph. Test directed graph for weak connectivity. Graph, node, and edge attributes are copied to the subgraphs by default. These are the top rated real world Python examples of networkx.weakly_connected_components extracted from open source projects. 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. 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 A sorted list of weakly connected, false otherwise there is a path from node! If you only want the largest component, it ’ s more efficient to use (. Data formats, Converting to and from other data formats for showing how to use networkx.weakly_connected_components ( ) Return! To every other node other data formats an example from Wikipedia: `` '' '' ''! 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