networkx package

Reference page for the bluegraph.backends.networkx package. All the interfaces below are also available as bluegraph.backends.networkx.<interface> (for example, from bluegraph.backends.networkx import NXPathFinder).

Graph metrics

class bluegraph.backends.networkx.analyse.metrics.NXMetricProcessor(pgframe=None, directed=True)

Class for metric processing based on NetworkX graphs.

betweenness_centrality(distance=None, write=False, write_property=None)

Compute (weighted) betweenness centrality.

closeness_centrality(distance=None, write=False, write_property=None)

Compute (weighted) closeness centrality.

degree_centrality(weight=None, write=False, write_property=None)

Compute (weighted) degree centrality.

pagerank_centrality(weight=None, write=False, write_property=None)

Compute (weighted) PageRank centrality.

Path search

class bluegraph.backends.networkx.analyse.paths.NXPathFinder(pgframe=None, directed=True)

NetworkX-based shortest paths finder.

get_distance(source, target, distance)

Get distance value between source and target.

get_subgraph_from_paths(paths)

Get a subgraph given the input paths.

minimum_spanning_tree(distance, write=False, write_property=None)

Compute the minimum spanning tree.

Parameters:

• distance (str) – Distance to minimize when computing the minimum spanning tree (MST)

• write (bool, optional) – Flag indicating whether the MST should be returned as a new graph object or saved within a Boolean edge property being True whenever a given edge belongs to the MST.

• write_property (str, optional) – Edge property name for marking edges beloning to the MST.

Returns:

tree – The minimum spanning tree graph object

Return type:

nx.Graph

bluegraph.backends.networkx.analyse.paths.handle_exclude_nx_edge(method)

Method decorator that removes and restores the direct s/t edge.

Community Detection

class bluegraph.backends.networkx.analyse.communities.NXCommunityDetector(pgframe=None, directed=True)

NetworkX-based community detection interface.

Currently supported community detection strategies for NetworkX:

• Louvain algorithm (strategy=”louvain”)

• Girvan–Newman algorithm (strategy=”girvan-newman”)

• Label propagation (strategy=”lpa”)

• Hierarchical clustering (strategy=”hierarchical”)

References

https://networkx.org/documentation/stable/reference/algorithms/community.html