Antagonism based Community Detection for Gephi
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README.md

Antagonism-based community detection

Developed by Alexis Jacomy for the Contropedia project. Plugin released under the MIT License.

This Gephi plugins aims to find communities when edges are connecting enemy nodes.

Here is how it works:

  1. The plugin computes a friendship score between each pair of nodes.
    • The more common enemies two nodes have, the higher their score will be.
    • The more connected two nodes are, the lower their score will become.
  2. All original edges are removed.
  3. Each pair of nodes that has a score higher than the threshold will become connected.
  4. The Modularity algorithm is then used to detect communities, on the new graph.
  5. Finally, unless the Override edges flag is set to true, the new edges are removed and the original ones are restored again. Be aware that if you set the Override edges flag the orginal network will be lost (unless you reload the orginal data).

Finally, you can represent the communities as colors through the Partition > Nodes panel (selecting Modularity).

Details of the algortihm for calculation the friendship score:

Parameters of the Algorithm:

  • pd = Direct Connection Penalty (should be a value below 0)
  • ct = Common neighbours score (transitive node, A->C->B)
  • cg = Common neighbours score (common traget node, A->C<-B)
  • co = Common neighbours score (common source node, A<-C->B)
  • c = Common neighbours score (undirected network, A-C-B). In the case of an undirected network the parameters ct, cg and co have to be set all to the same value (which we refer to as c from now on) otherwise the result of the algorithm will be depending on the internal representation of the undirected nodes in Gephi.
  • t = treshold for the establishement of a friendship link

Example

Network: A, B, C, D nodes; w1, w2, w3, w4, w5 edge weights

    D
   / \
 w4   w5
 /      \
A---w1---B
 \      /
 w2   w3
   \ /
    C

We calculate the friendship score s of A and B with the following formula (we only use an undirected network):

s(A, B) = pd * w1 + c * (w2 + w3 + w4 + w5)

And for A and C it is:

s(A, C) = pd * w2 + c * (w1 + w3)

All common neighbours of two nodes are contrasted with the weight multiplied by the penality factor pd of the conenctin between these two nodes.

A and B are linked if: s(A, B) > t