<scp>ProCom:</scp> A Few-shot Targeted Community Detection Algorithm-Reference-Cited by-同舟云学术

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ProCom: A Few-shot Targeted Community Detection Algorithm

Published:2024-08-24 Issue: Volume: Page:3414-3424
ISSN:
Container-title:Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
language:
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Author:

Wu Xixi¹^ORCID,Xiong Kaiyu¹^ORCID,Xiong Yun¹^ORCID,He Xiaoxin²^ORCID,Zhang Yao¹^ORCID,Jiao Yizhu³^ORCID,Zhang Jiawei⁴^ORCID

Affiliation:

1. Shanghai Key Laboratory of Data Science, School of Computer Science, Fudan University, Shanghai, China

2. National University of Singapore, Singapore, Singapore

3. University of Illinois at Urbana-Champaign, Champaign, Illinois, USA

4. IFM Lab, Department of Computer Science, University of California, Davis, Davis, California, USA

Funder

NSF (National Science Foundation)

National Natural Science Foundation of China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3637528.3671749

Reference52 articles.

1. Lars Backstrom Daniel P. Huttenlocher Jon M. Kleinberg and Xiangyang Lan. 2006. Group formation in large social networks: membership growth and evolution. In Knowledge Discovery and Data Mining.

2. Arjun Bakshi Srinivasan Parthasarathy and Kannan Srinivasan. 2018. Semi-Supervised Community Detection Using Structure and Size. In ICDM. 869--874.

3. Fast unfolding of communities in large networks

4. Tom B. Brown Benjamin Mann and Nick Ryder et al. 2020. Language Models are Few-Shot Learners. ArXiv Vol. abs/2005.14165 (2020).

5. Sandro Cavallari V. Zheng HongYun Cai K. Chang and E. Cambria. 2017. Learning Community Embedding with Community Detection and Node Embedding on Graphs. In CIKM.

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