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Poster
From which world is your graph
Cheng Li · Felix MF Wong · Zhenming Liu · Varun Kanade

Mon Dec 04 06:30 PM -- 10:30 PM (PST) @ Pacific Ballroom #217

Discovering statistical structure from links is a fundamental problem in the analysis of social networks. Choosing a misspecified model, or equivalently, an incorrect inference algorithm will result in an invalid analysis or even falsely uncover patterns that are in fact artifacts of the model. This work focuses on unifying two of the most widely used link-formation models: the stochastic block model (SBM) and the small world (or latent space) model (SWM). Integrating techniques from kernel learning, spectral graph theory, and nonlinear dimensionality reduction, we develop the first statistically sound polynomial-time algorithm to discover latent patterns in sparse graphs for both models. When the network comes from an SBM, the algorithm outputs a block structure. When it is from an SWM, the algorithm outputs estimates of each node's latent position.

Author Information

Cheng Li (College of William and Mary)
Felix MF Wong (Google)
Zhenming Liu (William and Mary)
Varun Kanade (University of Oxford)

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