Motifs in Big Networks: Methods and Applications

Abstract

Motifs have been recognized as basic network blocks and are found to be quite powerful in modeling certain patterns. Generally speaking, local characteristics of big networks could be reflected in network motifs. Over the years, motifs have attracted a lot of attention from researchers. However, most current literature reviews on motifs generally focus on the field of biological science. In contrast, here we try to present a comprehensive survey on motifs in the context of big networks. We introduce the definition of motifs and other related concepts. Big networks with motif-based structures are analyzed. Specifically, we respectively analyze four kinds of networks, including biological networks, social networks, academic networks, and infrastructure networks. We then examine methods for motif discovery, motif counting, and motif clustering. The applications of motifs in different areas have also been reviewed. Finally, some challenges and open issues in this direction are discussed.INDEX TERMS Network motif, motif counting, motif discovery, motif clustering, network science.

Ensemble Methods for Identifying RNA Operons and Regulons in the Clock Network of Neurospora Crassa

“…These network motifs are patterns of interconnected nodes, such as gene A regulating gene B, gene B regulating gene C, and gene A regulating gene C, that occur repeatedly in a large network. A formal definition is given [67]. As in integrated circuits, they may offer clues as to how large networks function [68].…”

A Data Transmission Algorithm Based on Triangle Link Structure Prediction in Opportunistic Social Networks

“…On the one hand, because the calculation of the high-order motif is more complicated, counting in large networks is often difficult and time-consuming to detect. On the other hand, due to the wide variety of high-level motifs structures, it is difficult to distinguish which ones are of practical significance [34]. Therefore, in order to achieve a balance between the time complexity and the practicality of the algorithm, this article does not consider the more complex opportunistic network structure.…”

On the Community Discovery Methods for Complex Networks: A Case Study

“…Network motifs were first proposed by [25] and can be formally denoted as M = {V M , E M } where V M is a set of m nodes and E M is a set of edges between m − 1 (line motif) and m(m−1) 2 (clique motif) in the motif M [26]. But generally a network motif consists between 3-8 number of nodes [27]. This is because higher-order motifs are structurally complicated.…”