论文标题
将异噬纳入图形神经网络中以进行图形分类
Incorporating Heterophily into Graph Neural Networks for Graph Classification
论文作者
论文摘要
图形神经网络（GNN）通常对图形分类具有强大的同质性，很少考虑异质，这意味着连接的节点往往具有不同的类标签和不同的特征。在实际情况下，图可能具有同质和异质性表现出的节点。未能概括此设置使许多GNN在图形分类中表现不佳。在本文中，我们通过识别三个有效的设计并开发出一种称为IHGNN的新型GNN体系结构来解决这一局限性（将杂物性融合到图神经网络中的缩写）。这些设计包括结合节点的自我和邻居插入的组合，来自不同层的节点嵌入的自适应聚集，以及用于构建图形读取函数的不同节点嵌入的不同层之间的分化。我们在各种图数据集上的经验验证了IHGNN，并证明它的表现优于最新的GNNs用于图形分类。
Graph Neural Networks (GNNs) often assume strong homophily for graph classification, seldom considering heterophily, which means connected nodes tend to have different class labels and dissimilar features. In real-world scenarios, graphs may have nodes that exhibit both homophily and heterophily. Failing to generalize to this setting makes many GNNs underperform in graph classification. In this paper, we address this limitation by identifying three effective designs and develop a novel GNN architecture called IHGNN (short for Incorporating Heterophily into Graph Neural Networks). These designs include the combination of integration and separation of the ego- and neighbor-embeddings of nodes, adaptive aggregation of node embeddings from different layers, and differentiation between different node embeddings for constructing the graph-level readout function. We empirically validate IHGNN on various graph datasets and demonstrate that it outperforms the state-of-the-art GNNs for graph classification.
