Towards Adaptive Neighborhood for Advancing Temporal Interaction Graph Modeling-Reference-Cited by-同舟云学术

Towards Adaptive Neighborhood for Advancing Temporal Interaction Graph Modeling

Published:2024-08-24 Issue: Volume:35 Page:4290-4301
ISSN:
Container-title:Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
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Author:

Zhang Siwei¹^ORCID,Chen Xi¹^ORCID,Xiong Yun¹^ORCID,Wu Xixi¹^ORCID,Zhang Yao¹^ORCID,Fu Yongrui¹^ORCID,Zhao Yinglong²^ORCID,Zhang Jiawei³^ORCID

Affiliation:

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

2. Ant Group, Shanghai, China

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

Funder

National Natural Science Foundation of China Projects

NSF (National Science Foundation)

Shanghai Science and Technology Development Fund

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3637528.3671877

Reference47 articles.

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3. Xi Chen, Yongxiang Liao, Yun Xiong, Yao Zhang, Siwei Zhang, Jiawei Zhang, and Yiheng Sun. 2023. SPEED: Streaming Partition and Parallel Acceleration for Temporal Interaction Graph Embedding. arXiv preprint arXiv:2308.14129 (2023).

4. Xi Chen, Siwei Zhang, Yun Xiong, Xixi Wu, Jiawei Zhang, Xiangguo Sun, Yao Zhang, Yinglong Zhao, and Yulin Kang. 2024. Prompt Learning on Temporal Interaction Graphs. arXiv preprint arXiv:2402.06326 (2024).

5. 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).