Heterogeneous Graph Mining

Heterogeneous Graph Mining is a subfield of graph mining that focuses on extracting meaningful patterns and knowledge from graphs comprising multiple types of nodes and edges, distinguished by its emphasis on leveraging type-level heterogeneity to enhance representation learning, inference, and predictive modeling.

116

Publications

5.9K

Citations

509

Authors

179

Institutions

Publications per year

2017–2026

92

Authors

509

  1. XL
    Xiaozhong Liu

    Indiana University Bloomington, United States

    6 publications · 141 citations

    6

    h-index

  2. CS
    Chuan Shi

    Beijing University of Posts and Telecommunications, China

    5 publications · 445 citations

    5

    h-index

  3. YS
    Yizhou Sun

    University of California, Los Angeles, United States

    4 publications · 155 citations

    4

    h-index

  4. LG
    Liangcai Gao

    Peking University, China

    4 publications · 107 citations

    4

    h-index

  5. CG
    Chun Guo

    Guizhou University, China

    3 publications · 91 citations

    3

    h-index

1–5 of 509

Venues

  1. Knowledge-Based Systems

    6 publications · 301 citations

    6

    h-index

  2. Briefings in Bioinformatics

    4 publications · 332 citations

    4

    h-index

  3. 4

    h-index

  4. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 3 publications · 121 citations

    3

    h-index

  5. BMC Bioinformatics

    2 publications · 42 citations

    2

    h-index

1–5