Emergence of community structures through biased random walks rewiring

TL;DR

This paper introduces a model of biased random walks in directed networks to explain community structures and their scaling behaviors, emphasizing the importance of local interactions and hidden layers in reproducing real-world network features.

Contribution

The paper presents a novel random walk-based model that captures community size distribution and other topological properties, highlighting the role of local interactions and hidden layers.

Findings

Model reproduces community size distribution

Explains degree and path length distributions

Highlights importance of hidden layers

Abstract

Community structures have been identified in various complex real-world networks, for example, communication, information, internet and shareholder networks. The scaling of community size distribution indicates the heterogeneity in the topological structures of the network. The current network generating or growing models can reproduce some properties, including degree distributions, large clustering coefficients and communities. However, the scaling behaviour of the community size lacks investigation, especially from the perspectives of local interactions. Based on the assumption that heterogeneous nodes behave differently and result in different topological positions of the networks, we propose a model of designed random walks in directed networks to explain the features in the observed networks. The model highlights that two different dynamics can mimic the local interactions, and a hidden layer is essential when reproducing the characteristics of real complex networks. The key features the model can explain include community size distribution, degree distribution, percolation properties, distribution of average path length and dependence of the above properties on the labels of nodes in the data.