Back to Search Start Over

Prioritized Propagation in Graph Neural Networks

Authors :
Cheng, Yao
Chen, Minjie
Li, Xiang
Shan, Caihua
Gao, Ming
Publication Year :
2023

Abstract

Graph neural networks (GNNs) have recently received significant attention. Learning node-wise message propagation in GNNs aims to set personalized propagation steps for different nodes in the graph. Despite the success, existing methods ignore node priority that can be reflected by node influence and heterophily. In this paper, we propose a versatile framework PPro, which can be integrated with most existing GNN models and aim to learn prioritized node-wise message propagation in GNNs. Specifically, the framework consists of three components: a backbone GNN model, a propagation controller to determine the optimal propagation steps for nodes, and a weight controller to compute the priority scores for nodes. We design a mutually enhanced mechanism to compute node priority, optimal propagation step and label prediction. We also propose an alternative optimization strategy to learn the parameters in the backbone GNN model and two parametric controllers. We conduct extensive experiments to compare our framework with other 11 state-of-the-art competitors on 8 benchmark datasets. Experimental results show that our framework can lead to superior performance in terms of propagation strategies and node representations.

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2311.02832
Document Type :
Working Paper