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Edge Label Inference in Generalized Stochastic Block Models: from Spectral Theory to Impossibility Results

Edge Label Inference in Generalized Stochastic Block Models: from Spectral Theory to Impossibility Results

This video was recorded at 27th Annual Conference on Learning Theory (COLT), Barcelona 2014. The classical setting of community detection consists of networks exhibiting a clustered structure. To more accurately model real systems we consider a class of networks (i) whose edges may carry labels and (ii) which may lack a clustered structure. Specifically we assume that nodes possess latent attributes drawn from a general compact space and edges between two nodes are randomly generated and labeled according to some unknown distribution as a function of their latent attributes. Our goal is then to infer the edge label distributions from a partially observed network. We propose a computationally efficient spectral algorithm and show it allows for asymptotically correct inference when the... Show More
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