A Multi-scale Graph Signature for Persistence Diagrams based on Return Probabilities of Random Walks. (arXiv:2209.14264v1 [cs.LG])

Persistence diagrams (PDs), often characterized as sets of death and birth of
homology class, have been known for providing a topological representation of a
graph structure, which is often useful in machine learning tasks. Prior works
rely on a single graph signature to construct PDs. In this paper, we explore
the use of a family of multi-scale graph signatures to enhance the robustness
of topological features. We propose a deep learning architecture to handle this
set input. Experiments on benchmark …

arxiv graph persistence random scale