NODE-SELECT: A Graph Neural Network Based On A Selective Propagation Technique
This addresses noise and scalability issues in GNNs for node classification, which is an incremental improvement over existing methods.
The paper tackles the problems of noise propagation and scalability in graph neural networks (GNNs) for node classification by proposing NODE-SELECT, a method that uses selective propagation layers to allow only the best-fitting nodes to share information. The result is that NODE-SELECT significantly outperformed existing GNNs in noise experiments and matched state-of-the-art results in experiments without noise across benchmark datasets.
While there exists a wide variety of graph neural networks (GNN) for node classification, only a minority of them adopt mechanisms that effectively target noise propagation during the message-passing procedure. Additionally, a very important challenge that significantly affects graph neural networks is the issue of scalability which limits their application to larger graphs. In this paper we propose our method named NODE-SELECT: an efficient graph neural network that uses subsetting layers which only allow the best sharing-fitting nodes to propagate their information. By having a selection mechanism within each layer which we stack in parallel, our proposed method NODE-SELECT is able to both reduce the amount noise propagated and adapt the restrictive sharing concept observed in real world graphs. Our NODE-SELECT significantly outperformed existing GNN frameworks in noise experiments and matched state-of-the art results in experiments without noise over different benchmark datasets.