Graph Filtration Learning
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Publication:6319414
arXiv1905.10996MaRDI QIDQ6319414
Author name not available (Why is that?)
Publication date: 27 May 2019
Abstract: 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.
Has companion code repository: https://github.com/c-hofer/graph_filtration_learning
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