On the Topology Awareness and Generalization Performance of Graph Neural Networks

TL;DR

This paper introduces a framework to analyze how the topological awareness of GNNs affects their ability to generalize, revealing that increased awareness can sometimes cause unfairness in structural group performance.

Contribution

It provides a comprehensive framework for characterizing GNN topology awareness and explores its impact on generalization, including theoretical insights and practical applications.

Findings

Increasing topology awareness may cause unfair generalization across groups

Empirical validation on benchmark datasets supports theoretical analysis

Framework applied to address cold start problem in graph active learning

Abstract

Many computer vision and machine learning problems are modelled as learning tasks on graphs where graph neural networks GNNs have emerged as a dominant tool for learning representations of graph structured data A key feature of GNNs is their use of graph structures as input enabling them to exploit the graphs inherent topological properties known as the topology awareness of GNNs Despite the empirical successes of GNNs the influence of topology awareness on generalization performance remains unexplored, particularly for node level tasks that diverge from the assumption of data being independent and identically distributed IID The precise definition and characterization of the topology awareness of GNNs especially concerning different topological features are still unclear This paper introduces a comprehensive framework to characterize the topology awareness of GNNs across any topological feature Using this framework we investigate the effects of topology awareness on GNN generalization performance Contrary to the prevailing belief that enhancing the topology awareness of GNNs is always advantageous our analysis reveals a critical insight improving the topology awareness of GNNs may inadvertently lead to unfair generalization across structural groups which might not be desired in some scenarios Additionally we conduct a case study using the intrinsic graph metric the shortest path distance on various benchmark datasets The empirical results of this case study confirm our theoretical insights Moreover we demonstrate the practical applicability of our framework by using it to tackle the cold start problem in graph active learning