Quantifying subtle structural differences between networks is challenging, as traditional methods often overlook the interplay between edges and nodes. Here, the authors introduce a dissimilarity measure based on network hierarchy entropy, which captures multiscale structural complexity and achieves high classification accuracy without feature engineering, demonstrating its utility across diverse applications, including evolving pattern analysis and protein classification.
- Jianhong Mou
- Longyun Wang
- Xin Lu