Unifying Graph Neural Networks across Spatial and Spectral Domains

Over recent years, Graph Neural Networks (GNNs) have garnered significant attention. However, the proliferation of diverse GNN models, underpinned by various theoretical approaches, complicates the process of model selection, as they are not readily comprehensible within a uniform framework. Specifically, early GNNs were implemented using spectral theory, while others were developed based on spatial theory . This divergence between spectral and spatial methodologies renders direct comparisons challenging. Moreover, the multitude of models within each domain further complicates the evaluation of their respective strengths and weaknesses.

In this half-day tutorial, we examine the state-of-the-art in GNNs and introduce a comprehensive framework that bridges the spatial and spectral domains, elucidating their complex interrelationship. This emphasis on a comprehensive framework enhances our understanding of GNN operations. The tutorial’s objective is to explore the interplay between key paradigms, such as spatial and spectral-based methods, through a synthesis of spectral graph theory and approximation theory. We provide an in-depth analysis of the latest research developments in GNNs in this tutorial, including discussions on emerging issues like over-smoothing. A range of well-established GNN models will be utilized to illustrate the universality of our proposed framework.