A parallel algorithm for mining maximal frequent subgraphs

Frequent graph mining has received a lot of attention from the research community because of the increasing availability of graph data in several domains, including bioinformatics, social networks, and cyber security. On large graphs such as protein-protein interaction and gene coexpression networks, frequent subgraph mining algorithms take hours to finish. In this paper, we propose a parallel algorithm for mining maximal frequent subgraphs from edge-attributed networks. Experiments on two real tissue-specific RNA-seq expression networks and synthetic data demonstrate the effectiveness of the proposed algorithm. Moreover, biological enrichment analysis of the reported patterns show that the patterns are biologically relevant and enriched with known biological processes and KEGG pathways.