Detecting genuine communities from large-scale social networks: A pattern-based method

被引：0
|
作者：
机构：
[1] Wu, Zhiang
[2] 1,Cao, Jie
[3] Wu, Junjie
[4] Wang, Youquan
[5] Liu, Chunyang
来源：
Liu, C. (lcy@isc.org.cn) | 1600年 / Oxford University Press卷 / 57期
关键词：
Community detection is a long-standing yet very difficult task in social network analysis. It becomes more challenging as many online social networking sites are evolving into super-large scales. Numerous methods have been proposed for community detection from massive networks; but how to reconcile the partitioning efficiency and the community quality remains an open problem. In this paper; we attempt to address this challenge by introducing a COSine-pattern-based COMmunity extraction framework: COSCOM.TheCOSCOMadopts an extracting view of community detection. It first extracts the so-called asymptotically equivalent structures (AESs) from networks; from which the nodes are further partitioned into crisp communities using any of the existing methods. Specifically; we prove that anAESis a very tight group of nodes; and is actually a cosine pattern defined by the extended cosine similarity.A novel cosine-pattern mining algorithm based on the ordered antimonotone of cosine similarity is thus proposed for the efficient extraction of AESs. Experiments on various real-world social networks demonstrate the advantage of the extracting view of community detection. In particular; COSCOM shows merits in detecting genuine communities by either internal or external validity. © The British Computer Society 2013;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Journal article (JA)
引用
收藏
相关论文
共 50 条
  • [21] Detecting communities in social networks based on cliques
    Nedioui, Med Abdelhamid
    Moussaoui, Abdelouahab
    Saoud, Bilal
    Babahenini, Mohamed Chaouki
    PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, 2020, 551
  • [22] Pattern-based Method for Anomaly Detection in Sensor Networks
    Ben Kraiem, Ines
    Ghozzi, Faiza
    Peninou, Andre
    Teste, Olivier
    PROCEEDINGS OF THE 21ST INTERNATIONAL CONFERENCE ON ENTERPRISE INFORMATION SYSTEMS (ICEIS), VOL 1, 2019, : 104 - 113
  • [23] A topic community-based method for friend recommendation in large-scale online social networks
    He, Chaobo
    Li, Hanchao
    Fei, Xiang
    Yang, Atiao
    Tang, Yong
    Zhu, Jia
    CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE, 2017, 29 (06):
  • [24] Maximal cliques based method for detecting and evaluating learning communities in social networks
    Adraoui, Meriem
    Retbi, Asmaa
    Idrissi, Mohammed Khalidi
    Bennani, Samir
    FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE, 2022, 126 : 1 - 14
  • [25] Method for visualizing information from large-scale carrier networks
    Tateishi, Naoki
    Tahara, Mitsuho
    Tanji, Naoyuki
    Seshake, Hikaru
    2013 15TH ASIA-PACIFIC NETWORK OPERATIONS AND MANAGEMENT SYMPOSIUM (APNOMS), 2013,
  • [26] Detecting Communities and Corresponding Central Nodes in Large Social Networks
    Jiang, Shengyi
    Wu, Meiling
    HIGH PERFORMANCE COMPUTING, 2013, 207 : 55 - 65
  • [27] Social Influence Analysis in Large-scale Networks
    Tang, Jie
    Sun, Jimeng
    Wang, Chi
    Yang, Zi
    KDD-09: 15TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, 2009, : 807 - 815
  • [28] Large-scale structural organization of social networks
    Motter, AE
    Nishikawa, T
    Lai, YC
    PHYSICAL REVIEW E, 2003, 68 (03)
  • [29] Analysis of large-scale social and information networks
    Kleinberg, Jon
    PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES, 2013, 371 (1987):
  • [30] Quantifying social segregation in large-scale networks
    Bjørn-Atle Reme
    Andreas Kotsadam
    Johannes Bjelland
    Pål Roe Sundsøy
    Jo Thori Lind
    Scientific Reports, 12
12345