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AdaPQ: Adaptive Exploration Product Quantization with Adversary-Aware Block Size Selection Toward Compression Efficiency Read chapter Read first chapter

2024 | OriginalPaper | Chapter

Improving Structural and Semantic Global Knowledge in Graph Contrastive Learning with Distillation

Authors : Mi Wen, Hongwei Wang, Yunsheng Xue, Yi Wu, Hong Wen

Published in: Advances in Knowledge Discovery and Data Mining

Publisher: Springer Nature Singapore

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Abstract

Graph contrastive learning has emerged as a pivotal task in the realm of graph representation learning, with the primary objective of maximizing mutual information between graph-augmented pairs exhibiting similar semantics. However, existing unsupervised graph contrastive learning approaches face a notable limitation in capturing both structural and semantic global information. This issues poses a substantial challenge, as nodes in close geographical proximity do not consistently possess similar features. To tackle this issue, this study introduces a simple framework for Distillation Node and Prototype Graph Contrastive Learning (DNPGCL). The framework enables contrastive learning by harnessing similar knowledge distillation to obtain more valuable structural and semantic global indications. Experimental results demonstrate that DNGCL outperforms existing unsupervised learning methods across a range of diverse graph datasets.

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previous chapter SD-Attack: Targeted Spectral Attacks on Graphs
next chapter DEGNN: Dual Experts Graph Neural Network Handling both Edge and Node Feature Noise
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Metadata
Title
Improving Structural and Semantic Global Knowledge in Graph Contrastive Learning with Distillation
Authors
Mi Wen
Hongwei Wang
Yunsheng Xue
Yi Wu
Hong Wen
Copyright Year
2024
Publisher
Springer Nature Singapore
Book
Advances in Knowledge Discovery and Data Mining

Print ISBN: 978-981-9722-52-5

Electronic ISBN: 978-981-9722-53-2

Copyright Year: 2024

DOI
https://doi.org/10.1007/978-981-97-2253-2_29

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