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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 32

Robust Detection of Link Communities in Large Social Networks by Exploiting Link Semantics

March 15, 2023

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Published Date: 2018-02-08

Registration: ISSN 2374-3468 (Online) ISSN 2159-5399 (Print)

Copyright: Published by AAAI Press, Palo Alto, California USA Copyright © 2018, Association for the Advancement of Artificial Intelligence All Rights Reserved.

Authors

Di Jin

Tianjin University


Xiaobao Wang

Tianjin University


Ruifang He

Tianjin University


Dongxiao He

Tianjin University


Jianwu Dang

Tianjin University


Weixiong Zhang

Washington University, St. Louis


DOI:

10.1609/aaai.v32i1.11283


Abstract:

Community detection has been extensively studied for various applications, focusing primarily on network topologies. Recent research has started to explore node contents to identify semantically meaningful communities and interpret their structures using selected words. However, links in real networks typically have semantic descriptions, e.g., comments and emails in social media, supporting the notion of communities of links. Indeed, communities of links can better describe multiple roles that nodes may play and provide a richer characterization of community behaviors than communities of nodes. The second issue in community finding is that most existing methods assume network topologies and descriptive contents to be consistent and to carry the compatible information of node group membership, which is generally violated in real networks. These methods are also restricted to interpret one community with one topic. The third problem is that the existing methods have used top ranked words or phrases to label topics when interpreting communities. However, it is often difficult to comprehend the derived topics using words or phrases, which may be irrelevant. To address these issues altogether, we propose a new unified probabilistic model that can be learned by a dual nested expectation-maximization algorithm. Our new method explores the intrinsic correlation between communities and topics to discover link communities robustly and extract adequate community summaries in sentences instead of words for topic labeling at the same time. It is able to derive more than one topical summary per community to provide rich explanations. We present experimental results to show the effectiveness of our new approach, and evaluate the quality of the results by a case study.

Topics: AAAI

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HOW TO CITE:

Di Jin||Xiaobao Wang||Ruifang He||Dongxiao He||Jianwu Dang||Weixiong Zhang Robust Detection of Link Communities in Large Social Networks by Exploiting Link Semantics Proceedings of the AAAI Conference on Artificial Intelligence, 32 (2018) .

Di Jin||Xiaobao Wang||Ruifang He||Dongxiao He||Jianwu Dang||Weixiong Zhang Robust Detection of Link Communities in Large Social Networks by Exploiting Link Semantics AAAI 2018, .

Di Jin||Xiaobao Wang||Ruifang He||Dongxiao He||Jianwu Dang||Weixiong Zhang (2018). Robust Detection of Link Communities in Large Social Networks by Exploiting Link Semantics. Proceedings of the AAAI Conference on Artificial Intelligence, 32, .

Di Jin||Xiaobao Wang||Ruifang He||Dongxiao He||Jianwu Dang||Weixiong Zhang. Robust Detection of Link Communities in Large Social Networks by Exploiting Link Semantics. Proceedings of the AAAI Conference on Artificial Intelligence, 32 2018 p..

Di Jin||Xiaobao Wang||Ruifang He||Dongxiao He||Jianwu Dang||Weixiong Zhang. 2018. Robust Detection of Link Communities in Large Social Networks by Exploiting Link Semantics. "Proceedings of the AAAI Conference on Artificial Intelligence, 32". .

Di Jin||Xiaobao Wang||Ruifang He||Dongxiao He||Jianwu Dang||Weixiong Zhang. (2018) "Robust Detection of Link Communities in Large Social Networks by Exploiting Link Semantics", Proceedings of the AAAI Conference on Artificial Intelligence, 32, p.

Di Jin||Xiaobao Wang||Ruifang He||Dongxiao He||Jianwu Dang||Weixiong Zhang, "Robust Detection of Link Communities in Large Social Networks by Exploiting Link Semantics", AAAI, p., 2018.

Di Jin||Xiaobao Wang||Ruifang He||Dongxiao He||Jianwu Dang||Weixiong Zhang. "Robust Detection of Link Communities in Large Social Networks by Exploiting Link Semantics". Proceedings of the AAAI Conference on Artificial Intelligence, 32, 2018, p..

Di Jin||Xiaobao Wang||Ruifang He||Dongxiao He||Jianwu Dang||Weixiong Zhang. "Robust Detection of Link Communities in Large Social Networks by Exploiting Link Semantics". Proceedings of the AAAI Conference on Artificial Intelligence, 32, (2018): .

Di Jin||Xiaobao Wang||Ruifang He||Dongxiao He||Jianwu Dang||Weixiong Zhang. Robust Detection of Link Communities in Large Social Networks by Exploiting Link Semantics. AAAI[Internet]. 2018[cited 2023]; .


ISSN: 2374-3468


Published by AAAI Press, Palo Alto, California USA
Copyright 2022, Association for the Advancement of
Artificial Intelligence 1900 Embarcadero Road, Suite
101, Palo Alto, California 94303 All Rights Reserved

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