MemMap: An Adaptive and Latent Memory Structure for Dynamic Graph Learning-Reference-Cited by-同舟云学术

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MemMap: An Adaptive and Latent Memory Structure for Dynamic Graph Learning

Published:2024-08-24 Issue: Volume:35 Page:1257-1268
ISSN:
Container-title:Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
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Author:

Ji Shuo¹^ORCID,Liu Mingzhe¹^ORCID,Sun Leilei²^ORCID,Liu Chuanren³^ORCID,Zhu Tongyu²^ORCID

Affiliation:

1. CCSE Lab, Beihang University, Beijing, China

2. CCSE Lab, Beihang University & Key Laboratory of Data Science and Intelligent Computing, International Innovation Institute, Beihang University, Beijing, China

3. The University of Tennessee, Knoxville, TN, USA

Funder

the National Natural Science Foundation of China

the Fundamental Research Funds for the Central Universities

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3637528.3672060

Reference33 articles.

1. Sequential Recommendation with Graph Neural Networks

2. 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).

3. Alexey Dosovitskiy Lucas Beyer Alexander Kolesnikov Dirk Weissenborn Xiaohua Zhai Thomas Unterthiner Mostafa Dehghani Matthias Minderer Georg Heigold Sylvain Gelly et al. 2020. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020).

4. dyngraph2vec: Capturing network dynamics using dynamic graph representation learning

5. Continuous-Time and Multi-Level Graph Representation Learning for Origin-Destination Demand Prediction

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