Graph Neural Networks with Learnable Structural and Positional Representations

Abstract

Graph neural networks (GNNs) have become the standard learning architectures for graphs. GNNs have been applied to numerous domains ranging from quantum chemistry, recommender systems to knowledge graphs and natural language processing. A major issue with arbitrary graphs is the absence of canonical positional information of nodes, which decreases the representation power of GNNs to distinguish e.g. isomorphic nodes and other graph symmetries. An approach to tackle this issue is to introduce Positional Encoding (PE) of nodes, and inject it into the input layer, like in Transformers. Possible graph PE are Laplacian eigenvectors. In this work, we propose to decouple structural and positional representations to make easy for the network to learn these two essential properties. We introduce a novel generic architecture which we call LSPE (Learnable Structural and Positional Encodings). We investigate several sparse and fully-connected (Transformer-like) GNNs, and observe a performance increase for molecular datasets, from 1.79% up to 64.14% when considering learnable PE for both GNN classes.

Dwivedi Vijay Prakash

PhD Student

I am a PhD student in Machine Learning at Nanyang Technological University, Singapore being supervised by Prof. Luu Anh Tuan (NTU) and Prof. Xavier Bresson (NUS).

Luu Anh Tuan

Assistant Professor

My research interests lie in the intersection of Artificial Intelligence and Natural Language Processing.