Graph Representation Learning with Individualization and Refinement

TL;DR

This paper introduces a novel graph neural network approach based on individualization and refinement, enabling richer representations and greater expressive power than traditional 1-WL GNNs, with improved performance on benchmarks.

Contribution

The work develops a learnable IR-based GNN that surpasses 1-WL expressiveness and balances complexity with richer node embeddings.

Findings

Outperforms 1-WL GNN models on benchmarks

More expressive than 1-WL test theoretically

Effective on synthetic and real datasets

Abstract

Graph Neural Networks (GNNs) have emerged as prominent models for representation learning on graph structured data. GNNs follow an approach of message passing analogous to 1-dimensional Weisfeiler Lehman (1-WL) test for graph isomorphism and consequently are limited by the distinguishing power of 1-WL. More expressive higher-order GNNs which operate on k-tuples of nodes need increased computational resources in order to process higher-order tensors. Instead of the WL approach, in this work, we follow the classical approach of Individualization and Refinement (IR), a technique followed by most practical isomorphism solvers. Individualization refers to artificially distinguishing a node in the graph and refinement is the propagation of this information to other nodes through message passing. We learn to adaptively select nodes to individualize and to aggregate the resulting graphs after refinement to help handle the complexity. Our technique lets us learn richer node embeddings while keeping the computational complexity manageable. Theoretically, we show that our procedure is more expressive than the 1-WL test. Experiments show that our method outperforms prominent 1-WL GNN models as well as competitive higher-order baselines on several benchmark synthetic and real datasets. Furthermore, our method opens new doors for exploring the paradigm of learning on graph structures with individualization and refinement.