如何使用networkX在有向加权图中检测社区？

Question

I have a directed, weighted graph one connected component and want to detect communities.

I have tried

community.best_partition(G)

to get a partition, however, I am getting the type error: Bad graph type, use only non directed graph .

I converted my graph to undirected and then got the partition. Is it some way to solve the problem without converting directed to undirected and use weights as well?

In addition, I have another question, how am I able to get top 5 centrality degree nodes in each community of partition? For example:

undirected_strong_partition = community.best_partition(undirectedstrong_community,weight='weight')
undirected_strong_partition

{'node1': 0,
 'node2': 0,
 'node3': 0,
 'node4': 0,
 'node5': 1,
 'node6': 2,
 'node7': 2,
 'node8': 2,
 'node9': 2,
...}

if it is possible, I want to get top 5 degree nodes in each community. for instance:

{community0: [nodetop1,nodetop2...nodetop5]}

Answer 1

I have a directed, weighted graph one connected component and want to detect communities.

I have tried

community.best_partition(G)

to get a partition, however, I am getting the type error: Bad graph type, use only non directed graph .

I converted my graph to undirected and then got the partition. Is it some way to solve the problem without converting directed to undirected and use weights as well?

In addition, I have another question, how am I able to get top 5 centrality degree nodes in each community of partition? For example:

undirected_strong_partition = community.best_partition(undirectedstrong_community,weight='weight')
undirected_strong_partition

{'node1': 0,
 'node2': 0,
 'node3': 0,
 'node4': 0,
 'node5': 1,
 'node6': 2,
 'node7': 2,
 'node8': 2,
 'node9': 2,
...}

if it is possible, I want to get top 5 degree nodes in each community. for instance:

{community0: [nodetop1,nodetop2...nodetop5]}