acc-Motif Detection Tool

TL;DR

This paper introduces new algorithms for exactly counting small directed network motifs of sizes 3, 4, and 5, demonstrating significantly faster performance than existing tools like FANMOD and Kavosh.

Contribution

The paper presents novel algorithms with improved time complexities for motif detection in directed graphs, and provides an efficient implementation available online.

Findings

Algorithms outperform FANMOD and Kavosh in speed

Exact counting of motifs of sizes 3, 4, and 5 achieved

Implementation is publicly accessible online

Abstract

Network motif algorithms have been a topic of research mainly after the 2002-seminal paper from Milo \emph{et al}, that provided motifs as a way to uncover the basic building blocks of most networks. In Bioinformatics, motifs have been mainly applied in the field of gene regulation networks. This paper proposes new algorithms to exactly count isomorphic pattern motifs of sizes 3, 4 and 5 in directed graphs. Let $G(V,E)$ be a directed graph with $m=|E|$. We describe an $O({m\sqrt{m}})$ time complexity algorithm to count isomorphic patterns of size 3. In order to count isomorphic patterns of size 4, we propose an $O(m^2)$ algorithm. To count patterns with 5 vertices, the algorithm is $O(m^2n)$. The new algorithms were implemented and compared with FANMOD and Kavosh motif detection tools. The experiments show that our algorithms are expressively faster than FANMOD and Kavosh's. We also let our motif-detecting tool available in the Internet.