Index-Based Biclique Percolation Communities Search on Bipartite Graphs

Biclique percolation community (BPC) search is a fundamental problem in bipartite graph analysis and have many applications. Existing online approach has to enumerate all the maximal bicliques and compute the results based on these bicliques. Considering the large number of maximal bicliques in real graphs and the high frequency of BPC search requests issued in real applications, existing approach is cost prohibitive to obtain the result. Motivated by this, we devise an index-based (BPC-Index) approach to address the problem. Based on the index, we can obtain the result in near-optimal time with well-bounded index space. We further devise an efficient index construction algorithm. Moreover, we also extend our indexing method to address the personalized BPC search problem, which is one of the most common variants of BPC search. We conduct extensive experiments on 10 real bipartite graphs, and the experimental results demonstrate the effectiveness of the BPC model, and the efficiency of our BPC search algorithms and index construction algorithms. Remarkably, our approach can achieve up to 8 orders of magnitude speedup compared to the existing online approach.