Foundations of Graph Neural Networks (A Logician's View) (Invited Paper)

Egor V. Kostylev

Abstract: Graph Neural Networks (GNNs) are a family of neural architectures that are naturally suited to learning functions on graphs. They are now used in a wide range of applications. It has been observed that GNNs share many similarities with classical computer science (CS) formalisms, such as the Weisfeiler-Leman graph isomorphism test, bisimulation, and logic. Most notably, both GNNs and these formalisms deal with functions on graphs and graph-like structures. This observation opens up an opportunity to compare GNN architectures with these formalisms in terms of different kinds of expressibility, thus positioning these architectures within the well-established landscape of theoretical CS. This, in turn, helps us better understand the fundamental capabilities and limitations of various GNN architectures, enabling more informed choices about which architecture to use - if any at all. In these lecture notes, I give an introduction to the state-of-the-art foundations of GNNs - specifically, our current understanding of their expressibility in terms of the classical formalisms, considering several notions of expressive power.

External IDs:dblp:conf/rweb/Kostylev25