python - Networkx Finding communities of directed graph -

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i graphing , visualizing directional social network. there statistic (modularity) in open source visualization tool called gephi (http://gephi.github.io/) allows me partition nodes separating them communities.

i'd accomplish task (finding communities) graph before visualize gephi can run various statistics on each community algorithm finds.

however, each community algorithm have found not operate on directed graphs. found 2 different algorithms work won't work digraphs:

  1. aynaud's community algorithm based on dendograms @ http://perso.crans.org/aynaud/communities/api.html
  2. networkx's own find k cliques @ https://networkx.github.io/documentation/latest/reference/generated/networkx.algorithms.community.kclique.k_clique_communities.html

this raises several questions, namely:

  1. is limitation of type of community finding algorithm want use?
  2. if so, gephi using algorithm removing directionality of graph before looking communities?
  3. are there other algorithms should looking for?
  4. are there other libraries solved?

thank you!

there several algorithms in machine learning classify nodes groups. there data or node name makes node distinctive part of 1 group or another. there might other ways break nodes besides data information number of links (which type of calculated data used in pagerank algorithm). there several try based on criteria support vector machines or ensemble methods random forests. there ways reduce dimentionality of data using principle component analysis. there excellent documentation on scikit-learn page. http://scikit-learn.org/stable/documentation.html


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