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兰州大学机构库  > 信息科学与工程学院  > 期刊论文
题名: Active Semi-Supervised Community Detection Based on Must-Link and Cannot-Link Constraints
作者: Cheng, JJ(程建军); Leng, MW; Li, LJ(李龙杰); Zhou, HH; Chen, XY(陈晓云)
收录类别: SCIE ; PubMed ; MEDLINE ; BIOSIS
出版日期: 2014-10-17
刊名: PLOS ONE
卷号: 9, 期号:10, 页码:-
出版者: PLOS
出版地: SAN FRANCISCO
英文摘要: Community structure detection is of great importance because it can help in discovering the relationship between the function and the topology structure of a network. Many community detection algorithms have been proposed, but how to incorporate the prior knowledge in the detection process remains a challenging problem. In this paper, we propose a semi-supervised community detection algorithm, which makes full utilization of the must-link and cannot-link constraints to guide the process of community detection and thereby extracts high-quality community structures from networks. To acquire the high-quality must-link and cannot-link constraints, we also propose a semi-supervised component generation algorithm based on active learning, which actively selects nodes with maximum utility for the proposed semi-supervised community detection algorithm step by step, and then generates the must-link and cannot-link constraints by accessing a noiseless oracle. Extensive experiments were carried out, and the experimental results show that the introduction of active learning into the problem of community detection makes a success. Our proposed method can extract high-quality community structures from networks, and significantly outperforms other comparison methods.
作者部门: [Cheng, Jianjun ; Leng, Mingwei ; Li, Longjie ; Zhou, Hanhai ; Chen, Xiaoyun] Lanzhou Univ, Sch Informat Sci & Engn, Lanzhou, Gansu, Peoples R China
通讯作者: Cheng, JJ (reprint author), Lanzhou Univ, Sch Informat Sci & Engn, Lanzhou, Gansu, Peoples R China.
学科分类: Science & Technology - Other Topics
文章类型: Article
所属项目编号: Fundamental Research Funds for the Central Universities [lzujbky-2014-54]
所属项目名称: 中央高校基本科研业务费专项资金
项目资助者: LZU
语种: 英语
DOI: 10.1371/journal.pone.0110088
ISSN号: 1932-6203
WOS记录号: WOS:000345204100044
PM记录号: 25329660
BIOSIS记录号: BIOSIS:PREV201500066556
第一机构:
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内容类型: 期刊论文
URI标识: http://ir.lzu.edu.cn/handle/262010/116280
Appears in Collections:信息科学与工程学院_期刊论文

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Recommended Citation:
Cheng, JJ,Leng, MW,Li, LJ,et al. Active Semi-Supervised Community Detection Based on Must-Link and Cannot-Link Constraints[J]. PLOS ONE,2014,9(10):-.
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