Research demonstrates effects of assortativity in real-world and generative networks, suggesting implications for network properties.
Key Points
This research aims to evaluate how assortativity influences network properties in both real-world and generative models.
Studied degree assortativity in real-world networks and generative models like Chung-Lu Graphs and GIRGs.
Analyzed various conditional and joint weight and degree distributions of connected nodes both numerically and mathematically.
Developed an extension of the GIRG model that displays controllable assortativity while preserving beneficial properties.
Confirmed that the Pearson assortativity coefficient is ineffective in measuring assortativity in heavy-tailed degree networks, with specific mathematical proof.
Identified that many real-world networks display assortative behavior, contrary to some generative models which are assortativity-neutral.
Presented a new model extension allowing for adjustable assortativity while maintaining the advantages of existing models.