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在線社交網(wǎng)絡(luò)群體發(fā)現(xiàn)研究進展

潘理,  吳鵬,  黃丹華

潘理, 吳鵬, 黃丹華. 在線社交網(wǎng)絡(luò)群體發(fā)現(xiàn)研究進展[J]. 電子與信息學報, 2017, 39(9): 2097-2107. doi: 10.11999/JEIT161192
引用本文: 潘理, 吳鵬, 黃丹華. 在線社交網(wǎng)絡(luò)群體發(fā)現(xiàn)研究進展[J]. 電子與信息學報, 2017, 39(9): 2097-2107. doi: 10.11999/JEIT161192
PAN Li, WU Peng, HUANG Danhua. Reviews on Group Detection in Online Social Networks[J]. Journal of Electronics & Information Technology, 2017, 39(9): 2097-2107. doi: 10.11999/JEIT161192
Citation: PAN Li, WU Peng, HUANG Danhua. Reviews on Group Detection in Online Social Networks[J]. Journal of Electronics & Information Technology, 2017, 39(9): 2097-2107. doi: 10.11999/JEIT161192

在線社交網(wǎng)絡(luò)群體發(fā)現(xiàn)研究進展

doi: 10.11999/JEIT161192
基金項目: 

國家自然科學基金(U1636105)，國家973計劃項目(2013CB329603)

Reviews on Group Detection in Online Social Networks

Funds: 

The National Natural Science Foundation of China (U1636105), The National 973 Program of China (2013CB 329603)

  • 摘要: 群體是在線社交網(wǎng)絡(luò)重要的中觀組織。群體發(fā)現(xiàn)不僅有重要的理論意義，還推動了在線社交網(wǎng)絡(luò)的應(yīng)用與發(fā)展，有廣泛的應(yīng)用前景。該文總結(jié)論述了在線社交網(wǎng)絡(luò)群體發(fā)現(xiàn)的研究進展。在分析群體形成機理的基礎(chǔ)上定義在線社交網(wǎng)絡(luò)群體，并介紹群體發(fā)現(xiàn)問題。根據(jù)挖掘群體時采用的不同特征，該文分別闡述基于個體屬性特征的群體發(fā)現(xiàn)方法和綜合屬性與結(jié)構(gòu)特征的群體發(fā)現(xiàn)方法。隨后從特征選取和檢測算法兩個方面重點介紹了惡意行為群體的發(fā)現(xiàn)方法。最后，對群體發(fā)現(xiàn)進一步的研究方向進行展望。
    Abstract: Groups are important mesoscopic organizations of Online Social Networks (OSNs). Group detection not only has important theoretical significance, but also has a wide range of applications. It promotes the application and development of online social networks. In this paper, group detection technology in online social networks is studied. Based on analyzing the formation mechanism of social groups, the online social network groups is defined and the group detection problem is introduced. According to different features adopted by group detection methods, the methods based on the attribute features only and those based on combination of attribute features and structure features are analyzed, respectively. Especially, it reviews the malicious behavior group detection methods by analyzing their feature selection mechanisms and detection models in detail. Finally, further research direction of group detection in online social networks is prospected.
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出版歷程
  • 收稿日期:  2016-11-04
  • 修回日期:  2017-02-26
  • 刊出日期:  2017-09-19

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