Multi-layer graph analysis for dynamic social networks

Abstract

Modern social networks frequently encompass multiple distinct types of connectivity information; for instance, explicitly acknowledged friend relationships might complement behavioral measures that link users according to their actions or interests. One way to represent these networks is as multi-layer graphs, where each layer contains a unique set of edges over the same underlying vertices (users). Edges in different layers typically have related but distinct semantics; depending on the application multiple layers might be used to reduce noise through averaging, to perform multifaceted analyses, or a combination of the two. However, it is not obvious how to extend standard graph analysis techniques to the multi-layer setting in a flexible way. In this paper we develop latent variable models and methods for mining multi-layer networks for connectivity patterns based on noisy data.

10 pages, 9 figures

References in corpus (2)

• Dynamic stochastic blockmodels: Statistical models for time-evolving networks
• Random Walks on Complex Networks

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• Information diffusion in interconnected heterogeneous networks