Computational modeling reveals distinct mixing patterns across diverse networks, indicating that assortative structures enhance percolation and robustness against vertex removal.
Key Points
To measure connection patterns between highly connected nodes across diverse real-world systems and evaluate how assortative mixing affects network percolation and resilience.
Quantified degree mixing patterns across real-world social, technological, and biological network datasets.
Developed a theoretical framework for assortatively mixed networks analyzed via analytical calculations and numerical simulations.
Social networks show predominantly assortative mixing, whereas technological and biological networks consistently trend toward disassortative mixing.
Assortative networks percolate more easily and maintain greater structural robustness when subjected to vertex removal compared to disassortative structures.