Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/140797
Title: Exploring communities in large profiled graphs
Authors: Chen, Yankai
Fang, Yixiang
Cheng, Reynold
Li, Yun
Chen, Xiaojun
Zhang, Jie
Keywords: Engineering::Computer science and engineering
Issue Date: 2018
Source: Chen, Y., Fang, Y., Cheng, R., Li, Y., Chen, X., & Zhang, J. (2019). Exploring communities in large profiled graphs. IEEE Transactions on Knowledge and Data Engineering, 31(8), 1624-1629. doi:10.1109/tkde.2018.2882837
Journal: IEEE Transactions on Knowledge and Data Engineering
Abstract: Given a graph G and a vertex q∈G, the community search (CS) problem aims to efficiently find a subgraph of G whose vertices are closely related to q. Communities are prevalent in social and biological networks, and can be used in product advertisement and social event recommendation. In this paper, we study profiled community search (PCS), where CS is performed on a profiled graph. This is a graph in which each vertex has labels arranged in a hierarchical manner. Extensive experiments show that PCS can identify communities with themes that are common to their vertices, and is more effective than existing CS approaches. As a naive solution for PCS is highly expensive, we have also developed a tree index, which facilitates efficient and online solutions for PCS.
URI: https://hdl.handle.net/10356/140797
ISSN: 1041-4347
DOI: 10.1109/TKDE.2018.2882837
Rights: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/TKDE.2018.2882837
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

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