GNN-Detective: Efficient Weakly Correlated Neighbors Distinguishing and Processing in GNN

Abstract

In the field of various downstream tasks of graph learning, graph neural networks (GNNs) have achieved the state-of-the-art (SOTA) performance benefits from its special propagation mechanism. The propagation mechanism aggregates attributes from neighbor nodes to obtain expressive node representations, which is pivotal for achieving SOTA performance in various downstream tasks. However, in most graph datasets, the neighborhood of each node may contain weakly correlated neighbors (WCNs), whose attributes may impair the expressiveness of central node representations. Though efforts have been devoted to solving such problem, they merely focus on aggregating fewer or even subtracting the attributes of WCNs. However, WCNs still share some correlated information with the central node, thus the correlated information provided by WCNs is underutilized. In this work, we devote to leveraging the correlated information provided by WCNs with our proposed method, namely GNN-detective. This detective can efficiently and automatically distinguish WCNs, as well as dig out their correlated information in the graph. It is realized by a semi-supervised learning framework, where the Differential Propagation (DP) module is designed specially for information triage and utilization. This module can fully leverage the correlated information provided by WCNs, and eliminate interference of uncorrelated information. We have conducted semi-supervised node classification tasks on 9 benchmark datasets. Our proposed method is proven to achieve the best performance in processing WCNs. The problems such as over-smoothing and overfitting are also mitigated as evaluated.