Towards adaptive graph neural networks via solving prior-data conflicts

Abstract

Graph neural networks (GNNs) have achieved remarkable performance in a variety of graph-related tasks. Recent evidence in the GNN community shows that such good performance can be attributed to the homophily prior; i.e., connected nodes tend to have similar features and labels. However, in heterophilic settings where the features of connected nodes may vary significantly, GNN models exhibit notable performance deterioration. In this work, we formulate this problem as prior-data conflict and propose a model called the mixture-prior graph neural network (MPGNN). First, to address the mismatch of homophily prior on heterophilic graphs, we introduce the non-informative prior, which makes no assumptions about the relationship between connected nodes and learns such relationship from the data. Second, to avoid performance degradation on homophilic graphs, we implement a soft switch to balance the effects of homophily prior and non-informative prior by learnable weights. We evaluate the performance of MPGNN on both synthetic and real-world graphs. Results show that MPGNN can effectively capture the relationship between connected nodes, while the soft switch helps select a suitable prior according to the graph characteristics. With these two designs, MPGNN outperforms state-of-the-art methods on heterophilic graphs without sacrificing performance on homophilic graphs.

摘要

图神经网络(GNN)在各种与图相关的任务中已取得显著性能。最近GNN社区的证据表明,这种良好的性能可归因于同质性先验,即连接的节点倾向于具有相似的特征和标签。然而,在异配性设置中,连接节点的特征可能会有显著变化,导致GNN模型性能明显下降。本文将此问题定义为先验数据冲突,提出一种名为混合先验图神经网络(MPGNN)的模型。首先,为解决异配图上同质性先验不匹配的问题,引入无信息先验,它不对连接节点之间的关系做任何假设,并从数据中学习这种关系。其次,为避免同质图上性能下降,通过可学习的权重实现软开关,以平衡同质性先验和非信息先验的影响。评估了MPGNN在合成图和真实世界图上的性能。结果表明,MPGNN能够有效捕捉连接节点之间的关系,而软开关有助于根据图的特征选择合适的先验。基于这两个设计,MPGNN在异配图上优于最先进的方法,而在同质图上不会牺牲性能。