grape.parallel_general_graph.ParallelGeneralGraph

class ParallelGeneralGraph

Class ParallelGeneralGraph for parallel implementation of directed graphs (DiGraph).

Constructs a new graph given an input file. A DiGraph stores nodes and edges with optional data or attributes. DiGraphs hold directed edges. Nodes can be arbitrary python objects with optional key/value attributes. Edges are represented as links between nodes with optional key/value attributes.

Initialize a graph with edges, name, or graph attributes.

Parameters

• incoming_graph_data (input graph (optional, default: None)) – Data to initialize graph. If None (default) an empty graph is created. The data can be an edge list, or any NetworkX graph object. If the corresponding optional Python packages are installed the data can also be a 2D NumPy array, a SciPy sparse matrix, or a PyGraphviz graph.

• attr (keyword arguments, optional (default= no attributes)) – Attributes to add to graph as key=value pairs.

See also

convert

Examples

>>> G = nx.Graph()  # or DiGraph, MultiGraph, MultiDiGraph, etc
>>> G = nx.Graph(name="my graph")
>>> e = [(1, 2), (2, 3), (3, 4)]  # list of edges
>>> G = nx.Graph(e)

Arbitrary graph attribute pairs (key=value) may be assigned

>>> G = nx.Graph(e, day="Friday")
>>> G.graph
{'day': 'Friday'}

__init__()

Initialize a graph with edges, name, or graph attributes.

Parameters

• incoming_graph_data (input graph (optional, default: None)) – Data to initialize graph. If None (default) an empty graph is created. The data can be an edge list, or any NetworkX graph object. If the corresponding optional Python packages are installed the data can also be a 2D NumPy array, a SciPy sparse matrix, or a PyGraphviz graph.

• attr (keyword arguments, optional (default= no attributes)) – Attributes to add to graph as key=value pairs.

See also

convert()

Examples

>>> G = nx.Graph()  # or DiGraph, MultiGraph, MultiDiGraph, etc
>>> G = nx.Graph(name="my graph")
>>> e = [(1, 2), (2, 3), (3, 4)]  # list of edges
>>> G = nx.Graph(e)

Arbitrary graph attribute pairs (key=value) may be assigned

>>> G = nx.Graph(e, day="Friday")
>>> G.graph
{'day': 'Friday'}

Methods

__init__()	Initialize a graph with edges, name, or graph attributes.
add_edge(u_of_edge, v_of_edge, **attr)	Add an edge between u and v.
add_edges_from(ebunch_to_add, **attr)	Add all the edges in ebunch_to_add.
add_node(node_for_adding, **attr)	Add a single node node_for_adding and update node attributes.
add_nodes_from(nodes_for_adding, **attr)	Add multiple nodes.
add_weighted_edges_from(ebunch_to_add[, weight])	Add weighted edges in ebunch_to_add with specified weight attr
adjacency()	Returns an iterator over (node, adjacency dict) tuples for all nodes.
betweenness_centrality_kernel(nodes, …)	Compute betweenness centrality, from shortest path list.
calculate_shortest_path()	Choose the most appropriate way to compute the all-pairs shortest path depending on graph size and density.
clear()	Remove all nodes and edges from the graph.
clear_data(attributes_to_remove)	Delete attributes for all nodes in the graph.
clear_edges()	Remove all edges from the graph without altering nodes.
closeness_centrality_kernel(nodes, …)	Compute betweenness centrality, from shortest path list.
compute_betweenness_centrality()	Betweenness_centrality calculation.
compute_closeness_centrality()	Closeness_centrality calculation.
compute_degree_centrality()	Degree centrality calculation.
compute_efficiency()	Efficiency calculation.
compute_indegree_centrality()	In-degree centrality calculation.
compute_local_efficiency()	Local efficiency calculation.
compute_nodal_efficiency()	Nodal efficiency calculation.
compute_outdegree_centrality()	Out-degree centrality calculation.
compute_service()	Compute service for every node, together with edge splitting.
construct_path_kernel(nodes, predecessor)	Reconstruct source-target paths starting from predecessors matrix, and populate the dictionary of shortest paths.
copy([as_view])	Returns a copy of the graph.
degree_centrality_kernel(nodes, graph_size)	Compute degree centrality.
dijkstra_iteration_parallel(out_queue, nodes)	Parallel SSSP algorithm based on Dijkstra’s method.
dijkstra_single_source_shortest_path()	Wrapper for parallel SSSP algorithm based on Dijkstra’s method.
edge_subgraph(edges)	Returns the subgraph induced by the specified edges.
efficiency_kernel(nodes, shortest_path_length)	Compute efficiency, starting from path length attribute.
floyd_warshall_initialization()	Initialization of Floyd Warshall APSP algorithm.
floyd_warshall_kernel(distance, predecessor, …)	Floyd Warshall’s APSP inner iteration.
floyd_warshall_predecessor_and_distance()	Parallel Floyd Warshall’s APSP algorithm.
get_edge_data(u, v[, default])	Returns the attribute dictionary associated with edge (u, v).
has_edge(u, v)	Returns True if the edge (u, v) is in the graph.
has_node(n)	Returns True if the graph contains the node n.
has_predecessor(u, v)	Returns True if node u has predecessor v.
has_successor(u, v)	Returns True if node u has successor v.
indegree_centrality_kernel(nodes, graph_size)	Compute in-degree centrality.
is_directed()	Returns True if graph is directed, False otherwise.
is_multigraph()	Returns True if graph is a multigraph, False otherwise.
load(filename)	Load input file.
local_efficiency_kernel(nodes, nodal_efficiency)	Compute local efficiency, starting from nodal efficiency attribute.
measure_iteration(nodes, record, kernel, …)	Inner iteration for parallel measures, to update shared dictionary.
measure_processes(record, kernel, *measure_args)	Division of total number of nodes in chuncks and parallel distribution of tasks into processes, for different kernel measure functions.
nbunch_iter([nbunch])	Returns an iterator over nodes contained in nbunch that are also in the graph.
neighbors(n)	Returns an iterator over successor nodes of n.
nodal_efficiency_kernel(nodes, efficiency, …)	Compute nodal efficiency, starting from efficiency attribute.
number_of_edges([u, v])	Returns the number of edges between two nodes.
number_of_nodes()	Returns the number of nodes in the graph.
order()	Returns the number of nodes in the graph.
outdegree_centrality_kernel(nodes, graph_size)	Compute out-degree centrality.
predecessors(n)	Returns an iterator over predecessor nodes of n.
remove_edge(u, v)	Remove the edge between u and v.
remove_edges_from(ebunch)	Remove all edges specified in ebunch.
remove_node(n)	Remove node n.
remove_nodes_from(nodes)	Remove multiple nodes.
reverse([copy])	Returns the reverse of the graph.
shortest_path_list_iteration(nodes, …)	Inner iteration for parallel shortest path list calculation, to update shared list.
shortest_path_list_kernel(nodes, shortest_path)	Collect the shortest paths that contain at least two nodes.
size([weight])	Returns the number of edges or total of all edge weights.
subgraph(nodes)	Returns a SubGraph view of the subgraph induced on nodes.
successors(n)	Returns an iterator over successor nodes of n.
to_directed([as_view])	Returns a directed representation of the graph.
to_directed_class()	Returns the class to use for empty directed copies.
to_undirected([reciprocal, as_view])	Returns an undirected representation of the digraph.
to_undirected_class()	Returns the class to use for empty undirected copies.
update([edges, nodes])	Update the graph using nodes/edges/graphs as input.

Attributes

adj	Graph adjacency object holding the neighbors of each node.
ParallelGeneralGraph.area
betweenness_centrality	Betweenness centrality of the graph.
closeness_centrality	Closeness centrality of the graph.
degree	A DegreeView for the Graph as G.degree or G.degree().
degree_centrality	Degree centrality of the graph.
description	description attribute for every node.
edges	An OutEdgeView of the DiGraph as G.edges or G.edges().
efficiency	Efficiency of the graph.
ParallelGeneralGraph.father_condition
final_status	final_status attribute for switches.
global_efficiency	Average global efficiency of the whole graph.
in_degree	An InDegreeView for (node, in_degree) or in_degree for single node.
in_edges	An InEdgeView of the Graph as G.in_edges or G.in_edges().
indegree_centrality	In-degree centrality of the graph.
init_status	init_status attribute for switches.
initial_service	initial_service attribute for every node.
local_efficiency	Local efficiency of the graph.
mark	mark attribute for every node.
mark_status	mark_status attribute for every node.
name	String identifier of the graph.
nodal_efficiency	Nodal efficiency of the graph.
nodes	A NodeView of the Graph as G.nodes or G.nodes().
out_degree	An OutDegreeView for (node, out_degree)
out_edges	An OutEdgeView of the DiGraph as G.edges or G.edges().
outdegree_centrality	Out-degree centrality of the graph.
ParallelGeneralGraph.perturbation_resistant
pred	Graph adjacency object holding the predecessors of each node.
service	Computed service.
shortest_path	Shortest existing paths between all node pairs.
shortest_path_length	Shortest path length.
sources	list of graph sources.
ParallelGeneralGraph.status_area
succ	Graph adjacency object holding the successors of each node.
switches	list of graph switches.
type	type attribute for every node.
users	list of graph users.
weight	weight attribute for every edge.