Dataflow matrix machines (by Anhinga anhinga)

Third talk: Haggai Maron (NVIDIA)

"Subgraph Aggregation Networks

Message-passing neural networks (MPNNs) are the leading architecture for deep learning on graph-structured data, in large part due to their simplicity and scalability. Unfortunately, it was shown that these architectures are limited in their expressive power. In order to gain more expressive power, a recent trend applies message-passing neural networks to subgraphs of the original graph. In this talk, I will present a representative framework of this family of methods, called Equivariant Subgraph Aggregation Networks (ESAN). The main idea behind ESAN is to represent each graph as a set of subgraphs derived from a predefined policy and to process the set of subgraphs using a suitable equivariant architecture. Our analysis shows that ESAN has favorable theoretical properties and that it performs well in practice. Following this, we will discuss some special properties of popular subgraph selection policies by connecting subgraph GNNs with previous work in equivariant deep learning."