Deep Graph Mapper: Seeing Graphs Through the Neural Lens.
Frontiers Big Data
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Bodnar, C., Cangea, C., & Lio, P. (2021). Deep Graph Mapper: Seeing Graphs Through the Neural Lens.. Frontiers Big Data, 4 680535-680535. https://doi.org/10.3389/fdata.2021.680535
Graph summarisation has received much attention lately, with various works tackling the challenge of defining pooling operators on data regions with arbitrary structures. These contrast the grid-like ones encountered in image inputs, where techniques such as max-pooling have been enough to show empirical success. In this work, we merge the Mapper algorithm with the expressive power of graph neural networks to produce topologically-grounded graph summaries. We demonstrate the suitability of Mapper as a topological framework for graph pooling by proving that Mapper is a generalisation of pooling methods based on soft cluster assignments. Building upon this, we show how easy it is to design novel pooling algorithms that obtain competitive results with other state-of-the-art methods. Additionally, we use our method to produce GNN-aided visualisations of attributed complex networks.
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External DOI: https://doi.org/10.3389/fdata.2021.680535
This record's URL: https://www.repository.cam.ac.uk/handle/1810/323644
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