  Recent advancements in graph representation learning have led to the
emergence of condensed encodings that capture the main properties of a graph.
However, even though these abstract representations are powerful for downstream
tasks, they are not equally suitable for visualisation purposes. In this work,
we merge Mapper, an algorithm from the field of Topological Data Analysis
(TDA), with the expressive power of Graph Neural Networks (GNNs) to produce
hierarchical, topologically-grounded visualisations of graphs. These
visualisations do not only help discern the structure of complex graphs but
also provide a means of understanding the models applied to them for solving
various tasks. We further demonstrate the suitability of Mapper as a
topological framework for graph pooling by mathematically proving an
equivalence with Min-Cut and Diff Pool. Building upon this framework, we
introduce a novel pooling algorithm based on PageRank, which obtains
competitive results with state of the art methods on graph classification
benchmarks.
