Unveiling Global Interactive Patterns across Graphs: Towards Interpretable Graph Neural Networks-Reference-Cited by-同舟云学术

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Unveiling Global Interactive Patterns across Graphs: Towards Interpretable Graph Neural Networks

Published:2024-08-24 Issue: Volume:13 Page:3277-3288
ISSN:
Container-title:Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
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Author:

Wang Yuwen¹^ORCID,Liu Shunyu²^ORCID,Zheng Tongya³^ORCID,Chen Kaixuan²^ORCID,Song Mingli²^ORCID

Affiliation:

1. School of Software Technology, Zhejiang University & State Key Laboratory of Blockchain and Security, Hangzhou, China

2. State Key Laboratory of Blockchain and Security, Zhejiang University & Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security, Hangzhou, China

3. Big Graph Center, School of Computer and Computing Science, Hangzhou City University & College of Computer Science and Technology, Zhejiang University, Hangzhou, China

Funder

Joint Funds of the Zhejiang Provincial Natural Science Foundation of China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3637528.3671838

Reference74 articles.

1. Molecular generative Graph Neural Networks for Drug Discovery

2. Karsten Borgwardt, Nicol Schraudolph, and SVN Vishwanathan. 2006. Fast computation of graph kernels. In Annual Conference on Neural Information Processing Systems.

3. Shortest-Path Kernels on Graphs

4. GraRep

5. Deep Neural Networks for Learning Graph Representations

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