29Oct
2016
Eugene / Learning, MIT Data Science: Data To Insights / 0 comment
Modularity Clustering
Method: define modularity score that we aim to maximise
Characteristic idea: compare communities to a random baseline graph that shares some properties with the actual graph, such as the number of edges and the degree of the nodes
1. Compute the number of edges within a community, then subtract the expected number of edges as per the baseline model ()
2. : if edge is between
: if there is no edge
Modularity score:
If score is large, then community is dense
Process:
1. Start with some partitioning, then move nodes between the groups to see if it improves the score
2. Use eigenvectors