Mastering Long-Tail Complexity on Graphs: Characterization, Learning, and Generalization-Reference-Cited by-同舟云学术

Mastering Long-Tail Complexity on Graphs: Characterization, Learning, and Generalization

Published:2024-08-24 Issue: Volume:3 Page:3045-3056
ISSN:
Container-title:Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
language:
Short-container-title:

Author:

Wang Haohui¹^ORCID,Jing Baoyu²^ORCID,Ding Kaize³^ORCID,Zhu Yada⁴^ORCID,Cheng Wei⁵^ORCID,Zhang Si⁶^ORCID,Fan Yonghui⁷^ORCID,Zhang Liqing¹^ORCID,Zhou Dawei¹^ORCID

Affiliation:

1. Virginia Tech, Blacksburg, USA

2. University of Illinois Urbana-Champaign, Urbana, USA

3. Northwestern University, Evanston, USA

4. IBM Research, Yorktown Heights, USA

5. NEC Labs America, Princeton, USA

6. Meta, Sunnyvale, USA

7. Amazon AGI, Sunnyvale, USA

Funder

Commonwealth Cyber Initiative

NSF (National Science Foundation)

Cisco Systems

4-VA

Defense Advanced Research Projects Agency

Department of Homeland Security

Deloitte & Touche LLP

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3637528.3671880

Reference69 articles.

1. Graph based anomaly detection and description: a survey

2. Deep Over-sampling Framework for Classifying Imbalanced Data

3. Rademacher and Gaussian complexities: Risk bounds and structural results;Bartlett Peter L;Journal of Machine Learning Research,2002

4. Aleksandar Bojchevski and Stephan Günnemann. 2018. Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking. In International Conference on Learning Representations (ICLR'18).

5. Kaidi Cao, Colin Wei, Adrien Gaidon, Nikos Arechiga, and Tengyu Ma. 2019. Learning imbalanced datasets with label-distribution-aware margin loss. In Advances in Neural Information Processing Systems (NeurIPS'19, Vol. 32).