Community Detection in Social Networks Considering Topic Correlations

Yingkui Wang, Di Jin, Katarzyna Musial, Jianwu Dang

AAAI 2019 pp. 321-328

doi:10.1609/AAAI.V33I01.3301321 /aaai/2019/wang2019aaai-community-a/

Abstract

Network contents including node contents and edge contents can be utilized for community detection in social networks. Thus, the topic of each community can be extracted as its semantic information. A plethora of models integrating topic model and network topologies have been proposed. However, a key problem has not been resolved that is the semantic division of a community. Since the definition of community is based on topology, a community might involve several topics. To ach

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Cite

Text

Wang et al. "Community Detection in Social Networks Considering Topic Correlations." AAAI Conference on Artificial Intelligence, 2019. doi:10.1609/AAAI.V33I01.3301321

Markdown

[Wang et al. "Community Detection in Social Networks Considering Topic Correlations." AAAI Conference on Artificial Intelligence, 2019.](https://mlanthology.org/aaai/2019/wang2019aaai-community-a/) doi:10.1609/AAAI.V33I01.3301321

BibTeX

@inproceedings{wang2019aaai-community-a,
  title     = {{Community Detection in Social Networks Considering Topic Correlations}},
  author    = {Wang, Yingkui and Jin, Di and Musial, Katarzyna and Dang, Jianwu},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2019},
  pages     = {321-328},
  doi       = {10.1609/AAAI.V33I01.3301321},
  url       = {https://mlanthology.org/aaai/2019/wang2019aaai-community-a/}
}