Exploring communities in large profiled graphs

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,...

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Main Authors: Chen, Yankai, Fang, Yixiang, Cheng, Reynold, Li, Yun, Chen, Xiaojun, Zhang, Jie
Other Authors: School of Computer Science and Engineering
Format: Article
Language:English
Published: 2020
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Online Access:https://hdl.handle.net/10356/140797
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1407972020-06-02T04:13:04Z Exploring communities in large profiled graphs Chen, Yankai Fang, Yixiang Cheng, Reynold Li, Yun Chen, Xiaojun Zhang, Jie School of Computer Science and Engineering Engineering::Computer science and engineering Community Search Social Networks 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. MOE (Min. of Education, S’pore) 2020-06-02T04:13:04Z 2020-06-02T04:13:04Z 2018 Journal Article 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 1041-4347 https://hdl.handle.net/10356/140797 10.1109/TKDE.2018.2882837 2-s2.0-85057366751 8 31 1624 1629 en IEEE Transactions on Knowledge and Data Engineering © 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
institution Nanyang Technological University
building NTU Library
country Singapore
collection DR-NTU
language English
topic Engineering::Computer science and engineering
Community Search
Social Networks
spellingShingle Engineering::Computer science and engineering
Community Search
Social Networks
Chen, Yankai
Fang, Yixiang
Cheng, Reynold
Li, Yun
Chen, Xiaojun
Zhang, Jie
Exploring communities in large profiled graphs
description 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.
author2 School of Computer Science and Engineering
author_facet School of Computer Science and Engineering
Chen, Yankai
Fang, Yixiang
Cheng, Reynold
Li, Yun
Chen, Xiaojun
Zhang, Jie
format Article
author Chen, Yankai
Fang, Yixiang
Cheng, Reynold
Li, Yun
Chen, Xiaojun
Zhang, Jie
author_sort Chen, Yankai
title Exploring communities in large profiled graphs
title_short Exploring communities in large profiled graphs
title_full Exploring communities in large profiled graphs
title_fullStr Exploring communities in large profiled graphs
title_full_unstemmed Exploring communities in large profiled graphs
title_sort exploring communities in large profiled graphs
publishDate 2020
url https://hdl.handle.net/10356/140797
_version_ 1681057486555054080