Statistical properties of community structure in large social and information networks
Proceedings of the 17th international conference on World Wide Web
Proceedings of the 23rd international conference on Supercomputing
A Study of Information Diffusion over a Realistic Social Network Model
CSE '09 Proceedings of the 2009 International Conference on Computational Science and Engineering - Volume 04
On finding graph clusterings with maximum modularity
WG'07 Proceedings of the 33rd international conference on Graph-theoretic concepts in computer science
Generation and analysis of large synthetic social contact networks
Winter Simulation Conference
Computer Science Review
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Clustering is useful in elucidating associations among agents of networks and has been advantageously applied in numerous fields including biology, chemistry, sociology, and economics. Most clustering algorithms have been applied to (weighted) networks with a fixed topology. However, many networks are constructed to simulate particular dynamics on them; e.g., transmission of disease, vehicular transportation, electricity supply, and economic transfers in financial markets. These dynamics affect the large-scale structure that emerges from the underlying network. We present a clustering method that incorporates not only the weighted network topology, but also the particular dynamics for an application domain. The approach is general and can be used with any dynamic process that can be simulated on a network. We apply this method to several networks to validate it: a benchmark network, various toy networks, and two large realistic synthetic networks. These span four, five, and two orders of magnitude in numbers of agents and links, and average degree, respectively, and possess vastly different degree distributions. The largest network includes over 580,000 agents and 13 million edges. The results show that different structures arise from variations in dynamics processes for a fixed network, and reflect the changes in the process itself. We observe a sharp transition from unclustered to well-clustered communities as dynamical parameters vary.