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Deep Graph Mapper: Seeing Graphs Through the Neural Lens.

Accepted version
Peer-reviewed

Type

Article

Change log

Authors

Bodnar, Cristian 
Cangea, Catalina 
Liò, Pietro 

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.

Description

Keywords

Journal Title

Frontiers Big Data

Conference Name

Journal ISSN

1535-6698

Volume Title

4

Publisher

Frontiers Media

Rights

All rights reserved
Sponsorship
EPSRC (EP/T017961/1)
NERC (2221169)