Propagation Kernels

TL;DR

Propagation kernels provide an efficient and versatile method for measuring graph similarity by tracking information flow, applicable to various graph types and scalable to large datasets, outperforming existing methods in speed without losing accuracy.

Contribution

We introduce propagation kernels, a novel graph kernel framework that leverages information propagation schemes for efficient and scalable graph similarity measurement across diverse graph types.

Findings

Propagation kernels are faster than state-of-the-art methods.

They work effectively on various graph types, including labeled and attributed graphs.

The approach scales to large graph databases with thousands of nodes.

Abstract

We introduce propagation kernels, a general graph-kernel framework for efficiently measuring the similarity of structured data. Propagation kernels are based on monitoring how information spreads through a set of given graphs. They leverage early-stage distributions from propagation schemes such as random walks to capture structural information encoded in node labels, attributes, and edge information. This has two benefits. First, off-the-shelf propagation schemes can be used to naturally construct kernels for many graph types, including labeled, partially labeled, unlabeled, directed, and attributed graphs. Second, by leveraging existing efficient and informative propagation schemes, propagation kernels can be considerably faster than state-of-the-art approaches without sacrificing predictive performance. We will also show that if the graphs at hand have a regular structure, for instance when modeling image or video data, one can exploit this regularity to scale the kernel computation to large databases of graphs with thousands of nodes. We support our contributions by exhaustive experiments on a number of real-world graphs from a variety of application domains.