Diffusion Fingerprints

TL;DR

This paper presents a novel diffusion-based method for classifying and clustering data modeled as directed graphs, capturing topological properties through diffusion vectors, and demonstrating high accuracy in metabolic network pathway extraction.

Contribution

The paper introduces a diffusion fingerprint technique that effectively classifies and clusters graph-structured data with minimal parameters and high computational efficiency.

Findings

Achieved state-of-the-art accuracy in metabolic pathway extraction

Developed a simple dimensionality reduction technique

Demonstrated the method's flexibility and power

Abstract

We introduce, test and discuss a method for classifying and clustering data modeled as directed graphs. The idea is to start diffusion processes from any subset of a data collection, generating corresponding distributions for reaching points in the network. These distributions take the form of high-dimensional numerical vectors and capture essential topological properties of the original dataset. We show how these diffusion vectors can be successfully applied for getting state-of-the-art accuracies in the problem of extracting pathways from metabolic networks. We also provide a guideline to illustrate how to use our method for classification problems, and discuss important details of its implementation. In particular, we present a simple dimensionality reduction technique that lowers the computational cost of classifying diffusion vectors, while leaving the predictive power of the classification process substantially unaltered. Although the method has very few parameters, the results we obtain show its flexibility and power. This should make it helpful in many other contexts.