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
  • [1] Detecting Genuine Communities from Large-Scale Social Networks: A Pattern-Based Method
    Wu, Zhiang
    Cao, Jie
    Wu, Junjie
    Wang, Youquan
    Liu, Chunyang
    COMPUTER JOURNAL, 2014, 57 (09): : 1343 - 1357
  • [2] Uncovering complex overlapping pattern of communities in large-scale social networks
    Elvis H.W. Xu
    Pak Ming Hui
    Applied Network Science, 4
  • [3] Uncovering complex overlapping pattern of communities in large-scale social networks
    Xu, Elvis H. W.
    Hui, Pak Ming
    APPLIED NETWORK SCIENCE, 2019, 4 (01)
  • [4] A GENETIC ALGORITHM FOR DETECTING COMMUNITIES IN LARGE-SCALE COMPLEX NETWORKS
    Shi, Chuan
    Yan, Zhenyu
    Wang, Yi
    Cai, Yanan
    Wu, Bin
    ADVANCES IN COMPLEX SYSTEMS, 2010, 13 (01): : 3 - 17
  • [5] Communication Pattern-based Distributed Snapshots in Large-Scale Systems
    Saker, Salem
    Agbaria, Adnan
    2015 IEEE 29TH INTERNATIONAL PARALLEL AND DISTRIBUTED PROCESSING SYMPOSIUM WORKSHOPS, 2015, : 1062 - 1071
  • [6] MRAttractor: Detecting Communities from Large-Scale Graphs
    Nguyen Vo
    Lee, Kyumin
    Thanh Tran
    2017 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA), 2017, : 797 - 806
  • [7] MEP - A Robust Algorithm for Detecting Communities in Large Scale Social Networks
    Zardi, Hedia
    Ben Romdhane, Lotfi
    2012 6TH INTERNATIONAL CONFERENCE ON SCIENCES OF ELECTRONICS, TECHNOLOGIES OF INFORMATION AND TELECOMMUNICATIONS (SETIT), 2012, : 13 - 19
  • [8] Communities Mining and Recommendation for Large-Scale Mobile Social Networks
    Yu, Ruiguo
    Wang, Jianrong
    Xu, Tianyi
    Gao, Jie
    Cao, Kunyu
    Yu, Mei
    WIRELESS ALGORITHMS, SYSTEMS, AND APPLICATIONS, WASA 2017, 2017, 10251 : 266 - 277
  • [9] A hierarchical visualization method for large-scale social networks
    Koshida, Minato
    Wakita, Ken
    Computer Software, 2011, 28 (02) : 202 - 216
  • [10] Fast Overlapping Communities Detection Algorithms for Large-Scale Social Networks
    Li Z.-L.
    Ji L.-X.
    Huang R.-Y.
    Lan J.-L.
    Tien Tzu Hsueh Pao/Acta Electronica Sinica, 2019, 47 (02): : 257 - 265
12345