面向同构的异构图重连
Homophily-oriented Heterogeneous Graph Rewiring
摘要：
随着万维网（WWW）的迅速发展，异构图（HG）得到了爆炸式的增长.近年来，异构图神经网络（HGNN）在HG学习方面表现出了巨大的潜力。目前对HGNN的研究主要集中在一些同质性较强的HG（由元路径连接的节点往往具有相同的标签）上，而对那些同质性较弱的HG的研究却很少。近年来，关于具有异质性的齐次图的研究已经很多。然而，由于异质性，这是不平凡的扩展他们的方法来处理HGs与heterophily。在这项工作中，基于经验的观察，我们提出了一个元路径诱导的度量来衡量一个HG的同伦程度。我们还发现，目前的HGNNs可能有退化的性能时，处理HGs的同质性较低。因此，有必要提高HGNN在非同质HGs上的推广能力。为此，我们提出了HDHGR，一种面向同质性的深度异构图重新布线方法，它修改了HG结构以提高HGNN的性能。我们从理论上验证了HDHGR。此外，在真实世界的HG上的实验证明了HDHGR的有效性，它带来了最多超过10%的相对增益。
英文摘要：
With the rapid development of the World Wide Web (WWW), heterogeneous graphs (HG) have explosive growth. Recently, heterogeneous graph neural network (HGNN) has shown great potential in learning on HG. Current studies of HGNN mainly focus on some HGs with strong homophily properties (nodes connected by meta-path tend to have the same labels), while few discussions are made in those that are less homophilous. Recently, there have been many works on homogeneous graphs with heterophily. However, due to heterogeneity, it is non-trivial to extend their approach to deal with HGs with heterophily. In this work, based on empirical observations, we propose a meta-path-induced metric to measure the homophily degree of a HG. We also find that current HGNNs may have degenerated performance when handling HGs with less homophilous properties. Thus it is essential to increase the generalization ability of HGNNs on non-homophilous HGs. To this end, we propose HDHGR, a homophily-oriented deep heterogeneous graph rewiring approach that modifies the HG structure to increase the performance of HGNN. We theoretically verify HDHGR. In addition, experiments on real-world HGs demonstrate the effectiveness of HDHGR, which brings at most more than 10% relative gain.
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