Graph community detection algorithm. This work is based on "Towards Linear Time Overlapping Community Detection in Social Networks" paper from Jierui Xie and Boleslaw K. Szymanski.

This project is part of ngraph family.

usage

// To find communities of graph `G`, where G is `ngraph.graph` object:
var findCommunities = require('ngraph.slpa');
var searchResult = findCommunities(G);

// now we can find all communites for node `foo`:
var nodeFooCommunities = searchResult.node.foo;

console.log('Node `foo` belongs to:');
for (var i = 0; i < nodeFooCommunities.length; ++i) {
  console.log(
    'Community  name:', nodeFooCommunities[i].name,
    'With probablity:', nodeFooCommunities[i].probablity
  );
}

// We can also find what other nodes belong to `foo`'s
// first community:
var firstCommunityName = nodeFooCommunities[0].name;
var otherNodesFromCommunity = searchResult.communities[firstCommunityName];

console.log('Community name:', firstCommunityName);
for (var j = 0; j < otherNodesFromCommunity.length; ++j) {
  console.log(otherNodesFromCommunity[j]);
}

According to the paper, performance of community calculation is O(T * m), where T is a number of iteration for the algorithm, and m is total number of edges. You can set number of iteration by passing it as a second argument:

var searchResult = findCommunities(G, 100);

Memory-wise current implementation is O(scary) and needs lots of improvements. I'm not happy with how I've implemented nodeMemory object, and I believe it can be done better.

install

With npm do:

npm install ngraph.slpa

license

MIT