Finding Theme Communities from Database Networks
Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 21_Publication in refereed journal › peer-review
Author(s)
Detail(s)
Original language | English |
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Pages (from-to) | 1071-1084 |
Journal / Publication | Proceedings of the VLDB Endowment |
Volume | 12 |
Issue number | 10 |
Publication status | Published - Jun 2019 |
Externally published | Yes |
Link(s)
DOI | DOI |
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Attachment(s) | Documents
Publisher's Copyright Statement
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Document Link | Links
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Link to Scopus | https://www.scopus.com/record/display.uri?eid=2-s2.0-85093628483&origin=recordpage |
Permanent Link | https://scholars.cityu.edu.hk/en/publications/publication(fb44c4bb-c25e-4123-afc3-d23473cc1364).html |
Abstract
Given a database network where each vertex is associated with a transaction database, we are interested in finding theme communities. Here, a theme community is a cohesive subgraph such that a common pattern is frequent in all transaction databases associated with the vertices in the subgraph. Finding all theme communities from a database network enjoys many novel applications. However, it is challenging since even counting the number of all theme communities in a database network is #P-hard. Inspired by the observation that a theme community shrinks when the length of the pattern increases, we investigate several properties of theme communities and develop TCFI, a scalable algorithm that uses these properties to effectively prune the patterns that cannot form any theme community. We also design TC-Tree, a scalable algorithm that decomposes and indexes theme communities efficiently. Retrieving a ranked list of theme communities from a TC-Tree of hundreds of millions of theme communities takes less than 1 second. Extensive experiments and a case study demonstrate the effectiveness and scalability of TCFI and TC-Tree in discovering and querying meaningful theme communities from large database networks.
Research Area(s)
Citation Format(s)
Finding Theme Communities from Database Networks. / Chu, Lingyang; Wang, Zhefeng; Pei, Jian et al.
In: Proceedings of the VLDB Endowment, Vol. 12, No. 10, 06.2019, p. 1071-1084.Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 21_Publication in refereed journal › peer-review
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