Beyond 1-WL with Local Ego-Network EncodingsDownload PDF

Published: 24 Nov 2022, Last Modified: 12 Mar 2024LoG 2022 PosterReaders: Everyone
Keywords: GNNs, message passing, weisfeiler-lehman, ego-networks, expressivity
Abstract: Identifying similar network structures is key to capture graph isomorphisms and learn representations that exploit structural information encoded in graph data. This work shows that ego-networks can produce a structural encoding scheme for arbitrary graphs with greater expressivity than the Weisfeiler-Lehman (1-WL) test. We introduce IGEL, a preprocessing step to produce features that augment node representations by encoding ego-networks into sparse vectors that enrich Message Passing (MP) Graph Neural Networks (GNNs) beyond 1-WL expressivity. We describe formally the relation between IGEL and 1-WL, and characterize its expressive power and limitations. Experiments show that IGEL matches the empirical expressivity of state-of-the-art methods on isomorphism detection while improving performance on seven GNN architectures.
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Type Of Submission: Extended abstract (max 4 main pages).
Type Of Submission: Extended abstract.
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