Comparing directed networks via denoising graphlet distributions

TL;DR

This paper extends the NetEmd network comparison method to directed networks by introducing a denoising approach using linear projections, improving performance especially for networks with size and density differences.

Contribution

The paper presents a novel extension of NetEmd for directed networks, incorporating denoising via linear projections to handle increased graphlet complexity.

Findings

Improved network comparison accuracy for directed graphs.

Enhanced performance when networks differ in size and density.

Effective denoising method for complex graphlet structures.

Abstract

Network comparison is a widely-used tool for analyzing complex systems, with applications in varied domains including comparison of protein interactions or highlighting changes in structure of trade networks. In recent years, a number of network comparison methodologies based on the distribution of graphlets (small connected network subgraphs) have been introduced. In particular, NetEmd has recently achieved state of the art performance in undirected networks. In this work, we propose an extension of NetEmd to directed networks and deal with the significant increase in complexity of graphlet structure in the directed case by denoising through linear projections. Simulation results show that our framework is able to improve on the performance of a simple translation of the undirected NetEmd algorithm to the directed case, especially when networks differ in size and density.