Deep Graph Mapper: Seeing Graphs Through the Neural Lens

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Bodnar, Cristian 
Cangea, Cătălina 
Liò, Pietro 

Graph summarization 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 generalization 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.

Big Data, mapper, graph neural networks, pooling, graph summarization, graph classification
Journal Title
Frontiers in Big Data
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Frontiers Media S.A.