Let be independent uniform random variables from , and consider the random variable . Computing the expectation is a routine computation: . However, there a slick way of computing this expectation. Let be another uniform random variable in . Consider the probability . On the one hand due to symmetry, it is equal to , on […]
Recently, I compiled a collection of applications that rely on dense subgraph discovery for my KDD’15 tutorial with Aris Gionis. In general, dense subgraph discovery is a key graph mining primitive. While by “dense” we generally mean subgraphs which are large enough and contain many edges, the exact notion of dense is application dependent. The […]
Latent feature learning: where overlapping communities, correlation clustering, binary matrix factorization and extremal graph theory meet Motivation: Suppose we are given the following. Agents: Five agents, represented by . Binary vertex features. An agent can either be interested or not in each out of three news categories business, entertainment, sports. Each interest is represented by a […]
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