TGOnline: Enhancing Temporal Graph Learning with Adaptive Online Meta-Learning-Reference-Cited by-同舟云学术

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TGOnline: Enhancing Temporal Graph Learning with Adaptive Online Meta-Learning

Published:2024-07-10 Issue: Volume: Page:1659-1669
ISSN:
Container-title:Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval
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Author:

Wang Ruijie¹^ORCID,Huang Jingyuan²^ORCID,Zhang Yutong³^ORCID,Li Jinyang¹^ORCID,Wang Yufeng¹^ORCID,Zhao Wanyu¹^ORCID,Liu Shengzhong⁴^ORCID,Mendis Charith¹^ORCID,Abdelzaher Tarek⁵^ORCID

Affiliation:

1. University of Illinois Urbana-Champaign, Champaign, IL, USA

2. Zhejiang University, Zhejiang, China

3. Stanford University, Stanford, CA, USA

4. Shanghai Jiao Tong University, Shanghai, China

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

Funder

Semiconductor Research Corporation

Army Research Laboratory

DoD Basic Research Office

Defense Advanced Research Projects Agency

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3626772.3657791

Reference56 articles.

1. Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, and Oksana Yakhnenko. Translating embeddings for modeling multi-relational data. In Advances in Neural Information Processing Systems, 2013.

2. Avishek Joey Bose, Ankit Jain, Piero Molino, and William L. Hamilton. Metagraph: Few shot link prediction via meta learning. CoRR, 2019.

3. Sihao Ding, Fuli Feng, Xiangnan He, Yong Liao, Jun Shi, and Yongdong Zhang. Causal incremental graph convolution for recommender system retraining. IEEE Transactions on Neural Networks and Learning Systems, 2022.

4. Tracking the Evolution of Communities in Dynamic Social Networks

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