Mining Network Motif Discovery by Learning Techniques

Abstract

Properties of complex networks represent a powerful set of tools that can be used to study the complex behaviour of these systems of interconnections. They can vary from properties represented as simplistic metrics (number of edges and nodes) to properties that reflect complex information of the connection between entities part of the network (assortativity degree, density or clustering coefficient). Such a topological property that has valuable implications on the study of the networks dynamics are network motifs - patterns of interconnections found in real-world networks. Knowing that one of the biggest issue with network motifs discovery is its algorithmic NP-complete nature, this paper intends to present a method to detect if a network is prone or not to generate motifs by making use of its topological properties while training various classification models. This approach wants to serve as a time saving pre-processing step for the state-of-the-art solutions used to detect motifs in Complex networks.