---
title: "Graph Invariant Kernels"
authors:
  - "Francesco Orsini"
  - "Paolo Frasconi"
  - "Luc De Raedt"
doi: ""
url: "https://ijcai.org/proceedings/2015/528"
pdf: "https://ijcai.org/proceedings/2015/0528.pdf"
venue: "Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence"
year: 2015
pages: "3756-3761"
track: "Special Track on Machine Learning"
keywords:
---

# Graph Invariant Kernels

Francesco Orsini, Paolo Frasconi, Luc De Raedt

*Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence*, pages 3756-3761.

## Abstract

We introduce a novel kernel that upgrades the Weisfeiler-Lehman and other graph kernels to effectively exploit high-dimensional and continuous vertex attributes. Graphs are first decomposed into subgraphs. Vertices of the subgraphs are then compared by a kernel that combines the similarity of their labels and the similarity of their structural role, using a suitable vertex invariant. By changing this invariant we obtain a family of graph kernels which includes generalizations of Weisfeiler-Lehman, NSPDK, and propagation kernels. We demonstrate empirically that these kernels obtain state-of-the-art results on relational data sets.