MASS-Based NemoProfile Construction for an Efficient Network Motif Search

A network motif is a frequent and unique subgraph pattern defined in a network and it has been applied in various biological and medical problems. However, finding network motifs is computationally intensive task as it involves heavily resource-demanding tasks. We have suggested a couple of parallelization efforts to alleviate the computational intensity in the past including MASS(Multi-Agent Spatial Simulation)-based and MapReduce-based methods. MASS library allows software agent to crawl a distributed array over a cluster system to find network motifs. Although MASS library can serve as a promising tool to parallelize graph algorithms intuitively with moderate performance penalty, current output formats that fail to provide instances of network motifs, are restricting extensible applications for real-world problems. Therefore, this paper introduces a MASS-based parallelization strategy to construct a new network motif representation called, NemoProfile (Network Motif Profile). A sequential implementation has showed that NemoProfile is a compact representation that can be almost effortlessly generated during sequential version of motif-finding process. In this paper, we also show that its effectiveness in various parallel implementations including MASS-Agent, MASS-Place, and MPI-based methods, by providing various testing results.