Graph Neural Networks with Learnable Structural and Positional RepresentationsDownload PDF

Published: 28 Jan 2022, Last Modified: 17 Sept 2023ICLR 2022 PosterReaders: Everyone
Keywords: graph neural networks, graph representation learning, transformers, positional encoding
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 \texttt{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.
One-sentence Summary: We propose a novel GNN architecture (LSPE) which decouples structural and positional representations to make easy for the network to learn the two essential properties.
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