Comparing directed networks via denoising graphlet distributions

Research output: Contribution to journalArticlepeer-review


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.
Original languageEnglish
JournalJournal of Complex Networks
Publication statusAccepted/In press - 10 Feb 2023


  • directed networks
  • network comparison
  • Network topology
  • principal component analysis
  • independent component analysis


Dive into the research topics of 'Comparing directed networks via denoising graphlet distributions'. Together they form a unique fingerprint.

Cite this