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https://hdl.handle.net/10356/98773
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DC Field | Value | Language |
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dc.contributor.author | Wang, Jia | en |
dc.contributor.author | Cheng, James | en |
dc.date.accessioned | 2013-09-09T08:19:16Z | en |
dc.date.accessioned | 2019-12-06T19:59:31Z | - |
dc.date.available | 2013-09-09T08:19:16Z | en |
dc.date.available | 2019-12-06T19:59:31Z | - |
dc.date.copyright | 2012 | en |
dc.date.issued | 2012 | en |
dc.identifier.uri | https://hdl.handle.net/10356/98773 | - |
dc.description.abstract | The k-truss is a type of cohesive subgraphs proposed recently for the study of networks. While the problem of computing most cohesive subgraphs is NP-hard, there exists a polynomial time algorithm for computing k-truss. Compared with k-core which is also efficient to compute, k-truss represents the "core" of a k-core that keeps the key information of, while filtering out less important information from, the k-core. However, existing algorithms for computing k-truss are inefficient for handling today's massive networks. We first improve the existing in-memory algorithm for computing k-truss in networks of moderate size. Then, we propose two I/O-efficient algorithms to handle massive networks that cannot fit in main memory. Our experiments on real datasets verify the efficiency of our algorithms and the value of k-truss. | en |
dc.language.iso | en | en |
dc.rights | © 2012 VLDB Endowment | en |
dc.subject | DRNTU::Engineering::Computer science and engineering | en |
dc.title | Truss decomposition in massive networks | en |
dc.type | Conference Paper | en |
dc.contributor.school | School of Computer Engineering | en |
dc.contributor.conference | Very Large Data Base Endowment (2012) | en |
dc.identifier.url | http://dl.acm.org/citation.cfm?id=2311909 | en |
item.fulltext | No Fulltext | - |
item.grantfulltext | none | - |
Appears in Collections: | SCSE Conference Papers |
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