Incremental k-core decomposition: algorithms and evaluation

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dc.citation.epage447en_US
dc.citation.issueNumber3en_US
dc.citation.spage425en_US
dc.citation.volumeNumber25en_US
dc.contributor.authorSarıyüce, A. E.en_US
dc.contributor.authorGedik, B.en_US
dc.contributor.authorJacques-Silva, G.en_US
dc.contributor.authorWu, Kun-Lungen_US
dc.contributor.authorCatalyurek, U.V.en_US
dc.date.accessioned2018-04-12T10:57:41Z
dc.date.available2018-04-12T10:57:41Z
dc.date.issued2016en_US
dc.departmentDepartment of Computer Engineeringen_US
dc.description.abstractA k-core of a graph is a maximal connected subgraph in which every vertex is connected to at least k vertices in the subgraph. k-core decomposition is often used in large-scale network analysis, such as community detection, protein function prediction, visualization, and solving NP-hard problems on real networks efficiently, like maximal clique finding. In many real-world applications, networks change over time. As a result, it is essential to develop efficient incremental algorithms for dynamic graph data. In this paper, we propose a suite of incremental k-core decomposition algorithms for dynamic graph data. These algorithms locate a small subgraph that is guaranteed to contain the list of vertices whose maximum k-core values have changed and efficiently process this subgraph to update the k-core decomposition. We present incremental algorithms for both insertion and deletion operations, and propose auxiliary vertex state maintenance techniques that can further accelerate these operations. Our results show a significant reduction in runtime compared to non-incremental alternatives. We illustrate the efficiency of our algorithms on different types of real and synthetic graphs, at varying scales. For a graph of 16 million vertices, we observe relative throughputs reaching a million times, relative to the non-incremental algorithms.en_US
dc.description.provenanceMade available in DSpace on 2018-04-12T10:57:41Z (GMT). No. of bitstreams: 1 bilkent-research-paper.pdf: 179475 bytes, checksum: ea0bedeb05ac9ccfb983c327e155f0c2 (MD5) Previous issue date: 2016en
dc.identifier.doi10.1007/s00778-016-0423-8en_US
dc.identifier.issn1066-8888
dc.identifier.urihttp://hdl.handle.net/11693/36932
dc.language.isoEnglishen_US
dc.publisherAssociation for Computing Machineryen_US
dc.relation.isversionofhttp://dx.doi.org/10.1007/s00778-016-0423-8en_US
dc.source.titleThe VLDB Journalen_US
dc.subjectDense subgraph discoveryen_US
dc.subjectIncremental graph algorithmsen_US
dc.subjectk-Coreen_US
dc.subjectStreaming graph algorithmsen_US
dc.subjectAlgorithmsen_US
dc.subjectComputational complexityen_US
dc.subjectCommunity detectionen_US
dc.subjectDecomposition algorithmen_US
dc.subjectDense subgraphen_US
dc.subjectGraph algorithmsen_US
dc.subjectIncremental algorithmen_US
dc.subjectMaximal clique findingsen_US
dc.subjectProtein function predictionen_US
dc.subjectGraph theoryen_US
dc.titleIncremental k-core decomposition: algorithms and evaluationen_US
dc.typeArticleen_US

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