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.

arXiv comment10 pages, 9 figures

References in corpus (8)

• Statistical mechanics of complex networks
• Fast algorithm for detecting community structure in networks
• Statistical Mechanics of Multiplex Ensembles: Entropy and Overlap
• Epidemic spreading on interconnected networks
• Growing multiplex networks
• Adaptive Evolutionary Clustering
• Dynamic stochastic blockmodels: Statistical models for time-evolving networks
• Random Walks on Complex Networks

Cited by in corpus (9)

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• Phase Transitions and a Model Order Selection Criterion for Spectral Graph Clustering
• Bayesian Learning of Dynamic Multilayer Networks
• Multilayer Spectral Graph Clustering via Convex Layer Aggregation: Theory and Algorithms
• AMOS: An Automated Model Order Selection Algorithm for Spectral Graph Clustering
• Joint Network Topology Inference via Structured Fusion Regularization
• Information diffusion in interconnected heterogeneous networks