AUTHOR=Bodnar Cristian , Cangea Cătălina , Liò Pietro 

TITLE=Deep Graph Mapper: Seeing Graphs Through the Neural Lens

JOURNAL=Frontiers in Big Data

VOLUME=Volume 4 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/big-data/articles/10.3389/fdata.2021.680535

DOI=10.3389/fdata.2021.680535

ISSN=2624-909X

ABSTRACT=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.