Attributed Community Detection

Attributed Community Detection is a community detection paradigm that extends traditional graph-based clustering by incorporating node or edge attributes to identify densely connected groups of nodes while simultaneously considering the homogeneity of their associated attribute information.

54

Publications

2.5K

Citations

181

Authors

102

Institutions

Publications per year

2017–2026

47

Authors

181

  1. XH
    Xin Huang

    Hong Kong Baptist University, Hong Kong SAR China

    5 publications · 576 citations

    5

    h-index

  2. JX
    Jeffrey Xu Yu

    Chinese University of Hong Kong, Hong Kong SAR China

    3 publications · 99 citations

    3

    h-index

  3. YL
    Yuefeng Li

    Queensland University of Technology, Australia

    2 publications · 184 citations

    2

    h-index

  4. ZL
    Zhixin Li

    Guangxi Normal University, China

    2 publications · 30 citations

    2

    h-index

  5. JH
    Jiafeng Hu

    University of Hong Kong, Hong Kong SAR China

    2 publications · 138 citations

    2

    h-index

1–5 of 181

Venues

  1. IEEE Transactions on Cybernetics

    4 publications · 273 citations

    4

    h-index

  2. Information Sciences

    3 publications · 148 citations

    3

    h-index

  3. Frontiers of Computer Science

    2 publications · 32 citations

    2

    h-index

  4. Neural Computing and Applications

    2 publications · 50 citations

    2

    h-index

  5. Expert Systems with Applications

    2 publications · 100 citations

    2

    h-index

1–5