Incremental k-core decomposition: algorithms and evaluation
dc.citation.epage | 447 | en_US |
dc.citation.issueNumber | 3 | en_US |
dc.citation.spage | 425 | en_US |
dc.citation.volumeNumber | 25 | en_US |
dc.contributor.author | Sarıyüce, A. E. | en_US |
dc.contributor.author | Gedik, B. | en_US |
dc.contributor.author | Jacques-Silva, G. | en_US |
dc.contributor.author | Wu, Kun-Lung | en_US |
dc.contributor.author | Catalyurek, U.V. | en_US |
dc.date.accessioned | 2018-04-12T10:57:41Z | |
dc.date.available | 2018-04-12T10:57:41Z | |
dc.date.issued | 2016 | en_US |
dc.department | Department of Computer Engineering | en_US |
dc.description.abstract | A 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.provenance | Made 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: 2016 | en |
dc.identifier.doi | 10.1007/s00778-016-0423-8 | en_US |
dc.identifier.issn | 1066-8888 | |
dc.identifier.uri | http://hdl.handle.net/11693/36932 | |
dc.language.iso | English | en_US |
dc.publisher | Association for Computing Machinery | en_US |
dc.relation.isversionof | http://dx.doi.org/10.1007/s00778-016-0423-8 | en_US |
dc.source.title | The VLDB Journal | en_US |
dc.subject | Dense subgraph discovery | en_US |
dc.subject | Incremental graph algorithms | en_US |
dc.subject | k-Core | en_US |
dc.subject | Streaming graph algorithms | en_US |
dc.subject | Algorithms | en_US |
dc.subject | Computational complexity | en_US |
dc.subject | Community detection | en_US |
dc.subject | Decomposition algorithm | en_US |
dc.subject | Dense subgraph | en_US |
dc.subject | Graph algorithms | en_US |
dc.subject | Incremental algorithm | en_US |
dc.subject | Maximal clique findings | en_US |
dc.subject | Protein function prediction | en_US |
dc.subject | Graph theory | en_US |
dc.title | Incremental k-core decomposition: algorithms and evaluation | en_US |
dc.type | Article | en_US |
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