  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 graph classification datasets demonstrate
that our proposed architecture outperforms other persistent homology-based
methods and achieves competitive performance compared to state-of-the-art
methods using graph neural networks. In addition, our approach can be easily
applied to large size of input graphs as it does not suffer from limited
scalability which can be an issue for graph kernel methods.
