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Poster
Graph Filtration Learning
Christoph Hofer · Florian Graf · Bastian Rieck · Marc Niethammer · Roland Kwitt

Thu Jul 16 01:00 PM -- 01:45 PM & Fri Jul 17 01:00 AM -- 01:45 AM (PDT) @ None #None

We propose an approach to learning with graph-structured data in the problem domain of graph classification. In particular, we present a novel type of readout operation to aggregate node features into a graph-level representation. To this end, we leverage persistent homology computed via a real-valued, learnable, filter function. We establish the theoretical foundation for differentiating through the persistent homology computation. Empirically, we show that this type of readout operation compares favorably to previous techniques, especially when the graph connectivity structure is informative for the learning problem.

Author Information

Christoph Hofer (University of Salzburg)
Florian Graf (University of Salzburg)
Bastian Rieck (ETH Zurich)
Marc Niethammer (UNC)
Roland Kwitt ("University of Salzburg, Austria")

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