计算机科学
图形
人工智能
维纳滤波器
机器学习
模式识别(心理学)
理论计算机科学
作者
Jiashun Cheng,Man Li,Jia Li,Fugee Tsung
出处
期刊:Proceedings of the ... AAAI Conference on Artificial Intelligence
[Association for the Advancement of Artificial Intelligence (AAAI)]
日期:2023-06-26
卷期号:37 (6): 7131-7139
被引量:6
标识
DOI:10.1609/aaai.v37i6.25870
摘要
Graph self-supervised learning (SSL) has been vastly employed to learn representations from unlabeled graphs. Existing methods can be roughly divided into predictive learning and contrastive learning, where the latter one attracts more research attention with better empirical performance. We argue that, however, predictive models weaponed with powerful decoder could achieve comparable or even better representation power than contrastive models. In this work, we propose a Wiener Graph Deconvolutional Network (WGDN), an augmentation-adaptive decoder empowered by graph wiener filter to perform information reconstruction. Theoretical analysis proves the superior reconstruction ability of graph wiener filter. Extensive experimental results on various datasets demonstrate the effectiveness of our approach.
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