Comparing directed networks via denoising graphlet distributions
arXiv:2207.09827 · doi:10.1093/comnet/cnad006
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.
References in corpus (11)
- Scikit-learn: Machine Learning in Python
- Emergence of scaling in random networks
- Auto-Encoding Variational Bayes
- Natural Scales in Geographical Patterns
- Cooperative Game Theory Approaches for Network Partitioning
- A Tutorial on Principal Component Analysis
- Hierarchical Organization in Complex Networks
- Scale-free networks are rare
- Biological network comparison using graphlet degree distribution
- Duplication-divergence model of protein interaction network
- A Survey on Subgraph Counting: Concepts, Algorithms and Applications to Network Motifs and Graphlets