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Regular version of the site

Book chapter

Learning Clusters through Information Diffusion

P. 3151-3157.
Prokhorenkova Liudmila, Tikhonov A., Litvak N.

When information or infectious diseases spread over a network, in many practical cases, one can observe when nodes adopt information or become infected, but the underlying network is hidden. In this paper, we analyze the problem of finding communities of highly interconnected nodes, given only the infection times of nodes. We propose, analyze, and empirically compare several algorithms for this task. The most stable performance, that improves the current state-of-the-art, is obtained by our proposed heuristic approaches, that are agnostic to a particular graph structure and epidemic model.

In book

Edited by: L. Ling, R. White. Vol. WWW ’19: The Web Conference 2019. NY: Association for Computing Machinery (ACM), 2019.