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On clustering network-valued data
Soumendu Sundar Mukherjee · Purnamrita Sarkar · Lizhen Lin

Mon Dec 04 06:30 PM -- 10:30 PM (PST) @ Pacific Ballroom #220

Community detection, which focuses on clustering nodes or detecting communities in (mostly) a single network, is a problem of considerable practical interest and has received a great deal of attention in the research community. While being able to cluster within a network is important, there are emerging needs to be able to \emph{cluster multiple networks}. This is largely motivated by the routine collection of network data that are generated from potentially different populations. These networks may or may not have node correspondence. When node correspondence is present, we cluster networks by summarizing a network by its graphon estimate, whereas when node correspondence is not present, we propose a novel solution for clustering such networks by associating a computationally feasible feature vector to each network based on trace of powers of the adjacency matrix. We illustrate our methods using both simulated and real data sets, and theoretical justifications are provided in terms of consistency.

Author Information

Soumendu Sundar Mukherjee (University of California, Berkeley)
Purnamrita Sarkar (UT Austin)
Lizhen Lin (The University of Texas at Austin)

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