Detection of network motifs by local concentration

TL;DR

This paper introduces a new statistical method for detecting network motifs based on local over-representation, which is efficient and reduces false positives, demonstrated on yeast gene interaction data.

Contribution

It proposes a novel local concentration-based statistic for motif detection that improves accuracy and efficiency over existing methods.

Findings

Successfully identified known biological motifs in yeast data

Reduced false positives in motif detection

Provided additional insights beyond existing methods

Abstract

Studying the topology of so-called {\em real networks}, that is networks obtained from sociological or biological data for instance, has become a major field of interest in the last decade. One way to deal with it is to consider that networks are built from small functional units called {\em motifs}, which can be found by looking for small subgraphs whose numbers of occurrences in the whole network of interest are surprisingly high. In this paper, we propose to define motifs through a local over-representation in the network and develop a statistic which allows us to detect them limiting the number of false positives and without time-consuming simulations. We apply it to the Yeast gene interaction data and show that the known biologically relevant motifs are found again and that our method gives some more information than the existing ones.