Percolation and epidemics in random clustered networks.

Area of Science:

• Network science
• Epidemiology
• Statistical physics

Background:

• Social networks exhibit clustered structures, influencing how infectious diseases spread.
• Percolation theory provides insights into epidemic dynamics on networks.
• Previous studies often conflated clustering with preferential attachment in high-degree nodes.

Purpose of the Study:

• To investigate the distinct effects of network clustering on percolation and epidemic thresholds.
• To compare random clustered networks with random unclustered networks having identical preferential mixing properties.

Main Methods:

• Introduction of a novel class of random clustered networks.
• Introduction of a comparable class of random unclustered networks.
• Analysis of percolation behavior and component sizes in both network types.

Main Results:

• Percolation in clustered networks leads to smaller component sizes compared to unclustered networks.
• Clustered networks demonstrate an increased epidemic threshold relative to unclustered networks.
• These effects are observed while maintaining the same preferential mixing in both network types.

Conclusions:

• Network clustering plays a significant role in modulating epidemic spread.
• Clustering acts to contain disease spread by reducing the size of connected components.
• Understanding network topology, specifically clustering, is crucial for accurate epidemic threshold prediction.