A position paper summarizing generative models, dynamic community detection, and community-aware immunization strategies, with the takeaway that exploiting community structure improves performance.
Community detection in networks without observing edges
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abstract
We develop a Bayesian hierarchical model to identify communities in networks for which we do not observe the edges directly, but instead observe a series of interdependent signals for each of the nodes. Fitting the model provides an end-to-end community detection algorithm that does not extract information as a sequence of point estimates but propagates uncertainties from the raw data to the community labels. Our approach naturally supports multiscale community detection as well as the selection of an optimal scale using model comparison. We study the properties of the algorithm using synthetic data and apply it to daily returns of constituents of the S&P100 index as well as climate data from US cities.
fields
physics.soc-ph 1years
2019 1verdicts
UNVERDICTED 1representative citing papers
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On community structure in complex networks: challenges and opportunities
A position paper summarizing generative models, dynamic community detection, and community-aware immunization strategies, with the takeaway that exploiting community structure improves performance.