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Abstract：
The graph autoencoder (GAE) is a powerful graph representation learning tool in an unsupervised learning manner for graph data. However, most existing GAE-based methods typically focus on preserving the graph topological structure by reconstructing the adjacency matrix while ignoring the preservation of the attribute information of nodes. Thus, the node attributes cannot be fully learned and the ability of the GAE to learn higher-quality representations is weakened. To address the issue, this paper proposes a novel GAE model that preserves node attribute similarity. The structural graph and the attribute neighbor graph, which is constructed based on the attribute similarity between nodes, are integrated as the encoder input using an effective fusion strategy. In the encoder, the attributes of the nodes can be aggregated both in their structural neighborhood and by their attribute similarity in their attribute neighborhood. This allows performing the fusion of the structural and node attribute information in the node representation by sharing the same encoder. In the decoder module, the adjacency matrix and the attribute similarity matrix of the nodes are reconstructed using dual decoders. The cross-entropy loss of the reconstructed adjacency matrix and the mean-squared error loss of the reconstructed node attribute similarity matrix are used to update the model parameters and ensure that the node representation preserves the original structural and node attribute similarity information. Extensive experiments on three citation networks show that the proposed method outperforms state-of-the-art algorithms in link prediction and node clustering tasks.
摘要：
图自动编码器（GAE）是一种强大的图表示学习工具，以无监督的方式针对图数据进行学习。然而,大多数现有的基于GAE的方法通常侧重于通过重建邻接矩阵来保留图的拓扑结构，而忽略了对节点属性信息的保留。因此，无法完全学习节点属性，并且GAE学习更高质量表示的能力被削弱。为了解决这个问题，本文提出了一种新颖的GAE模型，该模型保留了节点属性的相似性。结构图和属性近邻图，它是基于节点之间的属性相似性构建的，使用有效的融合策略集成为编码器输入。在编码器中，节点的属性既可以在其结构邻域中聚合，也可以通过其属性邻域中的属性相似性聚合。这允许通过共享相同的编码器来执行节点表示中的结构和节点属性信息的融合。在解码器模块中，使用双解码器重建节点的邻接矩阵和属性相似度矩阵。利用重构邻接矩阵的交叉熵损失和重构节点属性相似度矩阵的均方误差损失来更新模型参数,保证节点表示保留原始结构和节点属性相似度信息。在三个引文网络上的大量实验表明，该方法在链接预测和节点聚类任务中的性能优于最先进的算法。
