The Structurally Smoothed Graphlet Kernel

TL;DR

This paper introduces a novel smoothing technique for graphlet kernels that improves graph similarity measures by addressing sparsity and diagonal dominance issues, inspired by NLP smoothing methods.

Contribution

It extends Kneser-Ney and Pitman-Yor smoothing to graph motifs, enhancing graph kernel performance by leveraging sub-graph relationships.

Findings

Smoothed kernel outperforms raw frequency-based kernels.

Addresses sparsity and diagonal dominance in graphlet kernels.

Improves graph similarity measures in experiments.

Abstract

A commonly used paradigm for representing graphs is to use a vector that contains normalized frequencies of occurrence of certain motifs or sub-graphs. This vector representation can be used in a variety of applications, such as, for computing similarity between graphs. The graphlet kernel of Shervashidze et al. [32] uses induced sub-graphs of k nodes (christened as graphlets by Przulj [28]) as motifs in the vector representation, and computes the kernel via a dot product between these vectors. One can easily show that this is a valid kernel between graphs. However, such a vector representation suffers from a few drawbacks. As k becomes larger we encounter the sparsity problem; most higher order graphlets will not occur in a given graph. This leads to diagonal dominance, that is, a given graph is similar to itself but not to any other graph in the dataset. On the other hand, since lower order graphlets tend to be more numerous, using lower values of k does not provide enough discrimination ability. We propose a smoothing technique to tackle the above problems. Our method is based on a novel extension of Kneser-Ney and Pitman-Yor smoothing techniques from natural language processing to graphs. We use the relationships between lower order and higher order graphlets in order to derive our method. Consequently, our smoothing algorithm not only respects the dependency between sub-graphs but also tackles the diagonal dominance problem by distributing the probability mass across graphlets. In our experiments, the smoothed graphlet kernel outperforms graph kernels based on raw frequency counts.