PPT-Denser than the Densest Subgraph:
Author : tatiana-dople | Published Date : 2016-03-03
Extracting Optimal QuasiCliques with Quality Guarantees Charalampos Babis E Tsourakakis charalampostsourakakisaaltofi KDD 2013
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Denser than the Densest Subgraph:: Transcript
Extracting Optimal QuasiCliques with Quality Guarantees Charalampos Babis E Tsourakakis charalampostsourakakisaaltofi KDD 2013. There is a signi64257cant gap between the best known upper and lower bounds for this problem It is NPhard and does not have a PTAS unless NP has subexponential time algorithms On the other hand the current best known algorithm of Feige Kortsarz and Tsourakakis Francesco Bonchi Aristides Gionis Francesco Gullo Maria A Tsiarli Carnegie Mellon University Yahoo Research Aalto University University of Pittsburgh Pittsburgh PA USA Barcelona Spain Espoo Finland Pittsbu rgh PA USA ABSTRACT Finding den edu Ravi Kumar Yahoo Research Sunnyvale CA ravikumaryahooinccom Sergei Vassilvitskii Yahoo Research New York NY sergeiyahooinccom ABSTRACT The problem of nding locally dense components of a graph is an important primitive in data analysis with widera Both results are of similar avor ruling out constant factor approximations in polynomial time for the D S problem under an average case hardness assumption The rst result asserts that if Random AND formulas are hard to distinguish from ones that are Chen Rudolf Fleischer and Jian Li University of Notre Dame IN USA Email dchencsendedu Fudan University SCS and IIPL Shanghai China Email rudolffudaneducn University of Maryland College Park MD USA Email lijiancsumdedu Abstract We study approxi Liazi I Milis F Pascual and V Zissimopoulos Department of Informatics and Telecommunications University of Athens 157 84 Athens Greece mliazivassilis diuoagr Department of Informatics Athens University Economics and Business 104 34 Athens Greece 10 ].However,frequentlythedensestsubgraphprob-lemfailsindetectinglargenear-cliquesinnetworks.Inthiswork,weintroducethe-cliquedensestsubgraphproblem,2.Thisgeneralizesthewellstudieddens-estsubgraphprobl of Large Datasets. . Charalampos (Babis) E. Tsourakakis. Brown University. charalampos_tsourakakis@brown.edu. Brown University. of Large Datasets. . Charalampos (Babis) E. Tsourakakis. Brown University. charalampos_tsourakakis@brown.edu. Brown University. Algorithms. for . Analyzing Massive Graphs. Yubao. Wu. Case Western Reserve University. August 28, 2015. Graphs are Everywhere. Biological Networks. Social Networking Websites. Research Collaboration Network. (and related problems). on Minor-Free Graphs. Hans . Bodlaender. (U Utrecht, TU Eindhoven). Jesper. . Nederlof. (TU Eindhoven). Tom van der . Zanden. (U Utrecht). 1. Subgraph Isomorphism. Given: a . Extracting Optimal Quasi-Cliques with Quality . Guarantees. . Charalampos (Babis) E. Tsourakakis. charalampos.tsourakakis@aalto.fi. KDD 2013. Longin Jan Latecki. Based on :. P. . Dupont. , J. . Callut. , G. Dooms, J.-N. . Monette. and Y. Deville.. Relevant subgraph extraction from random walks in a graph. . RR 2006-07, Catholic University of Louvain , November 2006.. Subgraph. . with Perfect Completeness. Aviad Rubinstein (UC Berkeley). Mark . Braverman. , Young Kun . Ko. , and . Omri. Weinstein. A confession…. rest of workshop. this talk. vs . vs . SETH: SAT requires .
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