@inproceedings{950d3aa705cf4af8b8a23b2df4143a20,
title = "A Novel Graph Kernel Based on the Wasserstein Distance and Spectral Signatures",
abstract = "Spectral signatures have been used with great success in computer vision to characterise the local and global topology of 3D meshes. In this paper, we propose to use two widely used spectral signatures, the Heat Kernel Signature and the Wave Kernel Signature, to create node embeddings able to capture local and global structural information for a given graph. For each node, we concatenate its structural embedding with the one-hot encoding vector of the node feature (if available) and we define a kernel between two input graphs in terms of the Wasserstein distance between the respective node embeddings. Experiments on standard graph classification benchmarks show that our kernel performs favourably when compared to widely used alternative kernels as well as graph neural networks.",
keywords = "Graph kernel, Spectral signature, Wasserstein distance",
author = "Yantao Liu and Luca Rossi and Andrea Torsello",
note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.; Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition, S+SSPR 2022 ; Conference date: 26-08-2022 Through 27-08-2022",
year = "2022",
month = aug,
doi = "10.1007/978-3-031-23028-8_13",
language = "English",
isbn = "9783031230271",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "122--131",
editor = "Adam Krzyzak and Suen, {Ching Y.} and Nicola Nobile and Andrea Torsello",
booktitle = "Structural, Syntactic, and Statistical Pattern Recognition - Joint IAPR International Workshops, S+SSPR 2022, Proceedings",
address = "Germany",
}