Paper Info
Title
(Hyper)Graph Kernels Over Simplicial Complexes
Abstract
Graph kernels are one of the mainstream approaches when dealing with measuring similarity between graphs, especially for pattern recognition and machine learning tasks. In turn, graphs gained a lot of attention due to their modeling capabilities for several real-world phenomena ranging from bioinformatics to social network analysis. However, the attention has been recently moved towards hypergraphs, generalization of plain graphs where multi-way relations (other than pairwise relations) can be considered. In this paper, four (hyper)graph kernels are proposed and their efficiency and effectiveness are compared in a twofold fashion. First, by inferring the simplicial complexes on the top of underlying graphs and by performing a comparison among 18 benchmark datasets against state-of-the-art approaches; second, by facing a real-world case study (i.e., metabolic pathways classification) where input data are natively represented by hypergraphs. With this work, we aim at fostering the extension of graph kernels towards hypergraphs and, more in general, bridging the gap between structural pattern recognition and the domain of hypergraphs.
Year
DOI
Venue
2020
10.3390/e22101155
ENTROPY
Keywords
DocType
Volume
hypergraphs, graph kernels, kernel methods, support vector machines, simplicial complexes, topological data analysis
Journal
22
Issue
ISSN
Citations 
10
1099-4300
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Alessio Martino143.48
Antonello Rizzi236341.68