Pre-train and Refine: Towards Higher Efficiency in K-Agnostic Community Detection without Quality Degradation
Author:
Affiliation:
1. Department of CSE, HKUST, Hong Kong, Hong Kong
2. Theory Lab, Huawei, Hong Kong, Hong Kong
3. Theory Lab, Huawei, Beijing, China
Funder
Research Grants Council of Hong Kong
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3637528.3671686
Reference57 articles.
1. An algorithmic theory of learning: Robust concepts and random projection
2. LouvainNE
3. Fast unfolding of communities in large networks
4. Yuxuan Cao Jiarong Xu Carl J. Yang Jiaan Wang Yunchao Zhang Chunping Wang Lei Chen and Yang Yang. 2023. When to Pre-Train Graph Neural Networks? From Data Generation Perspective!. In SIGKDD. 142--153.
5. Jie Chen, Yousef Saad, and Zechen Zhang. 2022. Graph coarsening: from scientific computing to machine learning. SeMA Journal (2022), 1--37.
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