Graph neural networks are increasingly becoming the framework of choice for graph-based machine learning. In this paper, we propose a new graph neural network architecture that substitutes classical message passing with an analysis of the local distribution of node features. To this end, we extract the distribution of features in the egonet for each local neighbourhood and compare them against a set of learned label distributions by taking the histogram intersection kernel. The similarity information is then propagated to other nodes in the network, effectively creating a message passing-like mechanism where the message is determined by the ensemble of the features. We perform an ablation study to evaluate the network's performance under different choices of its hyper-parameters. Finally, we test our model on standard graph classification and regression benchmarks, and we find that it outperforms widely used alternative approaches, including both graph kernels and graph neural networks.

翻译：图神经网络正日益成为基于图的机器学习的主流框架。本文提出了一种新颖的图神经网络架构，该架构用节点特征局部分布的分析替代了传统的消息传递机制。为此，我们提取每个局部邻域中自我网络的特征分布，并通过直方图交核将其与一组学习到的标签分布进行比较。随后，相似性信息被传播至网络中的其他节点，从而形成一种由特征集成决定消息的消息传递类机制。我们通过消融实验评估了网络在不同超参数选择下的性能。最后，在标准图分类与回归基准测试中，我们发现该模型优于广泛使用的替代方法，包括图核和图神经网络。