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- W2963588303 abstract "Community detection in graphs is the problem of finding groups of vertices which are more densely connected than they are to the rest of the graph. This problem has a long history, but it is currently motivated by social and biological networks. While there are many ways to formalize it, one of the most popular is as an inference problem, where there is a planted “ground truth” community structure around which the graph is generated probabilistically. Our task is then to recover the ground truth knowing only the graph. Recently it was discovered, first h euristically i n p hysics a nd then rigorously in probability and computer science, that this problem has a phase transition at which it suddenly becomes impossible. Namely, if the graph is too sparse, or the probabilistic process that generates it is too noisy, then no algorithm can find a partition t hat i s correlated with the planted one—or even tell if there are communities, i.e., distinguish the graph from a purely random one with high probability. Above this information-theoretic threshold, there is a second threshold beyond which polynomial-time algorithms are known to succeed; in between, there is a regime in which community detection is possible, but conjectured to be exponentially hard. For computer scientists, this field offers a wealth of new ideas and open questions, with connections to probability and combinatorics, message-passing algorithms, and random matrix theory. Perhaps more importantly, it provides a window into the cultures of statistical physics and statistical inference, and how those cultures think about distributions of instances, landscapes of solutions, and hardness." @default.
- W2963588303 created "2019-07-30" @default.
- W2963588303 creator A5008033989 @default.
- W2963588303 date "2017-02-15" @default.
- W2963588303 modified "2023-09-27" @default.
- W2963588303 title "The Computer Science and Physics of Community Detection: Landscapes, Phase Transitions, and Hardness" @default.
- W2963588303 hasPublicationYear "2017" @default.
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