The suitability of standard hardware for Graph Neural Networks (GNNs) is an often overlooked issue in the Graph ML community. In this post, we explore the implementation of Temporal GNNs on a new hardware architecture developed by Graphcore that is tailored to graph-structured workloads. — This post was co-authored with Emanuele Rossi and Daniel Justus and is based on a collaboration with the British semiconductor company Graphcore.

Graph Neural Networks (GNNs) typically align their computation graph with the structure of the input graph. But are graphs the right computational fabric for GNNs? A recent line of papers challenges this assumption by replacing graphs with more general objects coming from the field of algebraic topology, which offer multiple theoretical and computational advantages. — This post was co-authored with Cristian Bodnar and Fabrizio Frasca and is based on the papers C. Bodnar, F. Frasca, et al., Weisfeiler and Lehman Go Topological: Message Passing Simplicial Networks (2021) ICML and C. Bodnar, F. Frasca et al., Weisfeiler and Lehman Go Cellular: CW Networks (2021) NeurIPS. It…

Graph Neural Networks (GNNs) are connected to diffusion equations that exchange information between the nodes of a graph. Being purely topological objects, graphs are implicitly assumed to have trivial geometry. Using a more general construction called cellular sheaves, we can endow the graph with a learnable geometric structure. We show that diffusion processes on cellular sheaves can solve any node classification task and can provide new insights about over-smoothing, the problem of dealing with heterophilic data, and the expressive power of GNNs.

Graph Machine Learning has become large enough of a field to deserve its own standalone event: the Learning on Graphs Conference (LoG). The inaugural event will take place in December 2022 and will be fully virtual and free to attend. We believe this event will stimulate research in the field by bringing together experts and practitioners from various subfields and adjacent disciplines. — This post was written by the Organising Committee and the Advisory Board of the LoG Conference. I am a member of the latter.

Physics-inspired continuous learning models on graphs allow to overcome the limitations of traditional GNNs — The message-passing paradigm has been the “battle horse” of deep learning on graphs for several years, making graph neural networks a big success in a wide range of applications, from particle physics to protein design. From a theoretical viewpoint, it established the link to the Weisfeiler-Lehman hierarchy, allowing to analyse the expressive power of GNNs. I argue that the “node and edge-centric” mindset of current graph deep learning schemes imposes insurmountable limitations that obstruct future progress in the field. As an alternative, I propose physics-inspired “continuous” learning models that open up a new trove of tools from the fields of differential geometry, algebraic topology, and differential equations so far largely unexplored in graph ML.