ComSim: A Bipartite Community Detection Algorithm Using Cycle and Node's Similarity

摘要

This study proposes ComSim, a new algorithm to detect communities in bipartite networks. This approach generates a partition of 丅 nodes by relying on similarity between the nodes in terms of links towards J⊥ nodes. In order to show the relevance of this approach, we implemented and tested the algorithm on 2 small datasets equipped with a ground-truth partition of the nodes. It turns out that, compared to 3 baseline algorithms used in the context of bipartite graph, ComSim proposes the best communities. In addition, we tested the algorithm on a large scale network. Results show that ComSim has good performances, close in time to Louvain. Besides, a qualitative investigation of the communities detected by ComSim reveals that it proposes more balanced communities.