新架构让图神经网络同时处理同质与异质数据，突破传统假设限制。
Redesigning graph filter-based GNNs to relax the homophily assumption
- 重新设计图滤波器角色，增强模型表达能力
- 在异质图上表现优于主流基线，避免过度平滑
- 理论证明具备置换等变性，适合复杂图结构
图神经网络（GNN）通常隐式依赖同质性图结构来学习不规则域数据。然而，许多实际应用涉及异质数据，导致现有GNN性能显著下降。为此，本文提出一种简单而有效的架构，重新诠释卷积GNN中图滤波器的作用，构建更通用的模型并引入更强归纳偏置。所提卷积层提升了模型表达能力，使其能有效学习同质与异质数据，同时缓解过度平滑问题。理论上，该架构具有置换等变性。实验表明，在同质与异质数据集上均优于多个先进基线，展现出良好潜力。
原文摘要 · Abstract (English)
Graph neural networks (GNNs) have become a workhorse approach for learning from data defined over irregular domains, typically by implicitly assuming that the data structure is represented by a homophilic graph. However, recent works have revealed that many relevant applications involve heterophilic data where the performance of GNNs can be notably compromised. To address this challenge, we present a simple yet effective architecture designed to mitigate the limitations of the homophily assumption. The proposed architecture reinterprets the role of graph filters in convolutional GNNs, resulting in a more general architecture while incorporating a stronger inductive bias than GNNs based on filter banks. The proposed convolutional layer enhances the expressive capacity of the architecture enabling it to learn from both homophilic and heterophilic data and preventing the issue of oversmoothing. From a theoretical standpoint, we show that the proposed architecture is permutation equivariant. Finally, we show that the proposed GNNs compares favorably relative to several state-of-the-art baselines in both homophilic and heterophilic datasets, showcasing its promising potential.
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