Densest Subgraph: Supermodularity and Iterative Peeling abstract
Abstract:
The densest subgraph problem in a graph (DSG), in the simplest form, is the following. Given an undirectedgraph G = (V, E) find a subset S of vertices that maximizes the ratio |E(S)|/|S| where E(S) is the setof edges with both endpoints in S. DSG and several of its variants are well-studied in theory and practiceand have several applications in data mining and network analysis. We study fast algorithms andstructural aspects of DSG via the lens of supermodularity. For this we consider the densest supermodularsubset problem (DSS): given a non-negative supermodular function f over a ground set V, maximize f(S)/|S|.Greedy peeling algorithms have been very popular for DSG and several variants due to their efficiency,empirical performance, and worst-case approximation guarantees. We describe a simple peeling algorithm forDSS and analyze its approximation guarantee in a fashion that unifies several existing results. Boob et al.developed an iterative peeling algorithm for DSG which appears to work very well in practice, and made aconjecture about its convergence to optimality. We affirmatively answer their conjecture, and in fact provethat a natural generalization of their algorithm converges for DSS. In subsequentwork we also showed that the algorithm converges to the densest supermodular set decomposition.The proofs are based on connections between the LP relaxation for DSG suggested by Charikar,Lovász extension of a supermodular function, Fujishige\'s result on lexicographically optimal basein a (contra) polymatroid, and continuous optimization algorithms such Frank-Wolfe method and MultiplicativeWeight Updates.Based on joint work in papers with Harb El Farouk, Kent Quanrud and Manuel Torres.
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