Graph neural networks (GNNs) have demonstrated success in modeling relational data, especially for data that exhibits homophily: when a connection between nodes tends to imply that they belong to the same class. However, while this assumption is true in many relevant situations, there are important real-world scenarios that violate this assumption, and this has spurred research into improving GNNs for these cases. In this work, we propose Evolving Computation Graphs (ECGs), a novel method for enhancing GNNs on heterophilic datasets. Our approach builds on prior theoretical insights linking node degree, high homophily, and inter vs intra-class embedding similarity by rewiring the GNNs' computation graph towards adding edges that connect nodes that are likely to be in the same class. We utilise weaker classifiers to identify these edges, ultimately improving GNN performance on non-homophilic data as a result. We evaluate ECGs on a diverse set of recently-proposed heterophilous datasets and demonstrate improvements over the relevant baselines. ECG presents a simple, intuitive and elegant approach for improving GNN performance on heterophilic datasets without requiring prior domain knowledge.

翻译：图神经网络（GNNs）在建模关系数据方面展现出显著成效，尤其适用于呈现同质性的数据：当节点间存在连接时，往往意味着它们属于同一类别。然而，尽管这一假设在众多相关场景中成立，但重要的现实世界案例中仍存在违背该假设的情况，这促使研究者探索如何改进GNNs以应对此类场景。本文提出演化计算图（ECGs），一种针对异质性数据集增强GNNs性能的新方法。该方法基于先前的理论洞见（关联节点度、高同质性、类间与类内嵌入相似性），通过重构GNNs的计算图，向可能属于同一类别的节点间添加连接。我们利用弱分类器识别这些连接，最终提升GNNs在非同质性数据上的表现。我们在近期提出的多种异质性数据集上评估了ECGs，并证明其相较于相关基准方法的优势。ECG提供了一种简单、直观且优雅的方法，无需先验领域知识即可提升GNNs在异质性数据集上的性能。