A new knowledge-based link recommendation approach using a non-parametric multilayer model of dynamic complex networks

Abstract

Traditionally, research on network theory focused on studying graphs with equivalent entities failing to deliberate the useful supplementary information related to the dynamic properties of the complex network interactions. This paper tries to study the evolution process of dynamic complex networks from a multilayer perspective by analyzing the properties of naturally multilayered web-based directed complex social networks of Google+ and Twitter, and undirected collaborative networks of DBLP and ASTRO-PH, thereby proposing a new non-parametric knowledge-based multilayer link recommendation approach. The paper investigates the layers’ evolution throughout the network evolution, inspects the evolution of each node's membership in different layers by an Infinite Factorial Hidden Markov Model, and finally formulates the intra-layer and inter-layer link generation process. Some Markov Chain Monte Carlo sampling strategies are driven to simulate parameters of the proposed multilayer model, using certain synthetic and real complex network datasets. Experimental results indicate great improvements in the performance of the proposed multilayer link recommendation approach in terms of certain analyzed performance measures.