High-Frequency-aware Hierarchical Contrastive Selective Coding for Representation Learning on Text Attributed Graphs-Reference-Cited by-同舟云学术

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High-Frequency-aware Hierarchical Contrastive Selective Coding for Representation Learning on Text Attributed Graphs

Published:2024-05-13 Issue: Volume:34 Page:4316-4327
ISSN:
Container-title:Proceedings of the ACM Web Conference 2024
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Author:

Zhang Peiyan¹^ORCID,Li Chaozhuo²^ORCID,Kang Liying³^ORCID,Huang Feiran⁴^ORCID,Wang Senzhang⁵^ORCID,Xie Xing²^ORCID,Kim Sunghun¹^ORCID

Affiliation:

1. Hong Kong University of Science and Technology, Hong Kong, Hong Kong

2. Microsoft Research Asia, Beijing, China

3. Hong Kong Polytechnic University, Hong Kong, Hong Kong

4. Jinan University, Guangzhou, China

5. Central South University, Changsha, China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3589334.3645614

Reference77 articles.

1. Leveraging Bidding Graphs for Advertiser-Aware Relevance Modeling in Sponsored Search

2. Beyond Low-frequency Information in Graph Convolutional Networks

3. Chen Cai and Yusu Wang. 2020. A note on over-smoothing for graph neural networks. arXiv preprint arXiv:2006.13318 (2020).

4. Measuring and Relieving the Over-Smoothing Problem for Graph Neural Networks from the Topological View

5. Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. 2020b. A simple framework for contrastive learning of visual representations. In International conference on machine learning. PMLR, 1597--1607.

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