Self-Explainable Temporal Graph Networks based on Graph Information Bottleneck-Reference-Cited by-同舟云学术

Self-Explainable Temporal Graph Networks based on Graph Information Bottleneck

Published:2024-08-24 Issue: Volume:33 Page:2572-2583
ISSN:
Container-title:Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
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Author:

Seo Sangwoo¹^ORCID,Kim Sungwon¹^ORCID,Jung Jihyeong¹^ORCID,Lee Yoonho¹^ORCID,Park Chanyoung¹^ORCID

Affiliation:

1. KAIST, Daejeon, Republic of Korea

Funder

National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT)

Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government(MSIT)

National Research Foundation of Korea(NRF) funded by Ministry of Science and ICT

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3637528.3671962

Reference45 articles.

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3. Weilin Cong, Si Zhang, Jian Kang, Baichuan Yuan, Hao Wu, Xin Zhou, Hanghang Tong, and Mehrdad Mahdavi. 2023. Do We Really Need Complicated Model Architectures For Temporal Networks? arXiv preprint arXiv:2302.11636 (2023).

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5. Finale Doshi-Velez and Been Kim. 2017. Towards a rigorous science of interpretable machine learning. arXiv preprint arXiv:1702.08608 (2017).