GNN-LoFI: a Novel Graph Neural Network through Localized Feature-based Histogram Intersection
This work addresses graph classification and regression tasks for researchers and practitioners in graph machine learning, representing an incremental improvement over existing methods.
The authors tackled the problem of graph-based machine learning by proposing a new graph neural network architecture that replaces classical message passing with localized feature distribution analysis, achieving performance that outperforms widely used alternative approaches on standard benchmarks.
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.