Deep Heterogeneous Graph Neural Networks via Similarity Regularization Loss and Hierarchical Fusion

Zhilong Xiong, Jia Cai
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Abstract

Recently, Graph Neural Networks (GNNs) have emerged as a promising and powerful method for tackling graph-structured data. However, most real-world graph-structured data contains distinct types of objects (nodes) and links (edges), which is called heterogeneous graph. The heterogeneity and rich semantic information indeed increase the difficulties in handling heterogeneous graph. Most of the current heterogeneous graph neural networks (HeteGNNs) can only build on a very shallow structure. This is caused by a phenomenon called semantic confusion, where the node embeddings become indistinguishable with the growth of model depth, leading to the degradation of the model performance. In this paper, we address this problem by proposing a similarity regularization loss and hierarchical fusion based heterogeneous graph neural networks (SHGNN). The hierarchical fusion strategy is utilized to fuse the features of the node embeddings at each layer, which can improve the expressive power of the model, and then a similarity regularization loss is introduced, by which the problem of indistinguishability among nodes can be alleviated. Our approach is flexible to combine various HeteGNNs effectively. Experimental results on real-world heterogeneous graph-structured data demonstrate the state-of-the-art performance of the proposed approach, which can efficiently mitigate the semantic confusion problem.
基于相似性正则化损失和层次融合的深度异构图神经网络
最近,图神经网络(gnn)作为处理图结构数据的一种有前途和强大的方法而出现。然而,大多数现实世界的图结构数据包含不同类型的对象(节点)和链接(边),这被称为异构图。异构性和丰富的语义信息确实增加了异构图处理的难度。目前大多数异构图神经网络(hetegnn)只能建立在一个非常浅的结构上。这是由一种称为语义混淆的现象引起的,其中节点嵌入随着模型深度的增长而变得无法区分,导致模型性能下降。本文提出了一种基于相似性正则化损失和层次融合的异构图神经网络(SHGNN)来解决这一问题。采用分层融合策略融合各层节点嵌入的特征,提高模型的表达能力;引入相似度正则化损失,缓解节点间不可分辨的问题。我们的方法是灵活的,可以有效地组合各种hetegnn。在实际异构图结构数据上的实验结果表明,该方法能够有效地缓解语义混淆问题。
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