Mining Maximal Frequent Dense Subgraphs without Candidate Maintenance in PPI Networks

The prediction of protein function is one of the most challenging problems in bioinformatics. Several studies have shown that the prediction using PPI is promising. However, the PPI data generated from high-throughput experiments are very noisy, which renders great challenges to the existing methods. In this paper, we propose an algorithm, MFC, to efficiently mine maximal frequent dense subgraphs without candidate maintenance in PPI networks. It adopts several techniques to achieve efficient mining. We evaluate our approach on four human PPI data sets. The experimental results show our approach has good performance in terms of efficiency.