On Finding Dense Common Subgraphs

We study the recently introduced problem of finding dense common subgraphs: Given a sequence of graphs that share the same vertex set, the goal is to find a subset of vertices $S$ that maximizes some aggregate measure of the density of the subgraphs induced by $S$ in each of the given graphs. Different choices for the aggregation function give rise to variants of the problem that were studied recently. We settle many of the questions left open by previous works, showing NP-hardness, hardness of approximation, non-trivial approximation algorithms, and an integrality gap for a natural relaxation.

[1]  Jiawei Han,et al.  Mining coherent dense subgraphs across massive biological networks for functional discovery , 2005, ISMB.

[2]  Sanjeev Arora,et al.  Computational complexity and information asymmetry in financial products , 2011, Commun. ACM.

[3]  Guy Kortsarz On the Hardness of Approximating Spanners , 2001, Algorithmica.

[4]  Kevin A. Lai,et al.  Label optimal regret bounds for online local learning , 2015, COLT.

[5]  Andrew V. Goldberg,et al.  Finding a Maximum Density Subgraph , 1984 .

[6]  Erik Norlander,et al.  Finding the Densest Common Subgraph with Linear Programming , 2016 .

[7]  Dimitris Fotakis,et al.  On the Size and the Approximability of Minimum Temporally Connected Subgraphs , 2016, ICALP.

[8]  Jonas Holmerin,et al.  Clique Is Hard to Approximate within n1-o(1) , 2000, ICALP.

[9]  Devdatt P. Dubhashi,et al.  Lovász ϑ function, SVMs and finding dense subgraphs , 2013, J. Mach. Learn. Res..

[10]  Niko Beerenwinkel,et al.  Finding Dense Subgraphs in Relational Graphs , 2015, ECML/PKDD.

[11]  Evaggelia Pitoura,et al.  Best Friends Forever (BFF): Finding Lasting Dense Subgraphs , 2016, ArXiv.

[12]  Moses Charikar,et al.  Greedy approximation algorithms for finding dense components in a graph , 2000, APPROX.

[13]  Francesco Bonchi,et al.  Core Decomposition and Densest Subgraph in Multilayer Networks , 2017, CIKM.

[14]  Avi Wigderson,et al.  Public-key cryptography from different assumptions , 2010, STOC '10.

[15]  J Gómez-Gardeñes,et al.  k-core percolation on multiplex networks. , 2014, Physical review. E, Statistical, nonlinear, and soft matter physics.

[16]  Venkatesan Guruswami,et al.  A New Multilayered PCP and the Hardness of Hypergraph Vertex Cover , 2005, SIAM J. Comput..

[17]  Moses Charikar,et al.  On Approximating Target Set Selection , 2016, APPROX-RANDOM.

[18]  David Zuckerman,et al.  Electronic Colloquium on Computational Complexity, Report No. 100 (2005) Linear Degree Extractors and the Inapproximability of MAX CLIQUE and CHROMATIC NUMBER , 2005 .