Applying the community detection methods on a real data set to be able to determine the applicability of these methods on real data and to determine how community detection is useful in real life application. So, testing the previous learned methods on the WormNetv3 is big Biological Network of sparse networks which integrates of all data-type-specific networks (CE-CX, CE-GN, CE-GT, CE-HT, CE-LC, CE-PG, DM-CX, DM-HT, DM-LC, DR-CX, HS-CX, HS-HT, HS-LC, SC-CC, SC-CX, SC-HT, SC-LC, SC-TS) by modified Bayesian integration. Betweenness centrality method which is very similar to the GN method was applied at the data set and was able to discover communities and relations between genes which can be very important for relating and discovering the relations between these genes’ groups. The algorithm was implemented to calculate unweighted shortest paths between all pairs of nodes in the network.
WormNetv3 network size is 16347 genes 762822 links