Siamese Attribute-missing Graph Auto-encoder
Wenxuan Tu, Sihang Zhou, Yue Liu, Xinwang Liu

TL;DR
This paper introduces SAGA, a novel graph auto-encoder that enhances attribute and structure embedding interaction for attribute-missing graphs, improving data completion through a siamese network and structural constraints.
Contribution
The paper proposes a siamese network-based auto-encoder with structural constraints to better integrate attribute and structure information in attribute-missing graphs.
Findings
Outperforms state-of-the-art methods on six benchmark datasets.
Effectively captures high-order discriminative features.
Improves latent feature quality for data completion.
Abstract
Graph representation learning (GRL) on attribute-missing graphs, which is a common yet challenging problem, has recently attracted considerable attention. We observe that existing literature: 1) isolates the learning of attribute and structure embedding thus fails to take full advantages of the two types of information; 2) imposes too strict distribution assumption on the latent space variables, leading to less discriminative feature representations. In this paper, based on the idea of introducing intimate information interaction between the two information sources, we propose our Siamese Attribute-missing Graph Auto-encoder (SAGA). Specifically, three strategies have been conducted. First, we entangle the attribute embedding and structure embedding by introducing a siamese network structure to share the parameters learned by both processes, which allows the network training to benefit…
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Taxonomy
TopicsAdvanced Graph Neural Networks
MethodsSiamese Network · SAGA
