Temporal-topological properties of higher-order evolving networks.

Area of Science:

• Network Science
• Computational Social Science
• Data Analysis

Background:

• Human social interactions are often modeled as pairwise, time-varying connections.
• Group interactions, or higher-order events, are crucial but less studied in temporal network analysis.
• Existing methods primarily focus on dyadic (two-person) interactions over time.

Purpose of the Study:

• To develop methods for characterizing temporal-topological properties of higher-order events in evolving networks.
• To compare different types of real-world networks based on these higher-order interactions.
• To understand how network structure influences interaction patterns across different contexts.

Main Methods:

• Proposed novel methods to quantify temporal and topological features of group interactions (higher-order events).
• Analyzed 8 real-world physical contact networks and 5 collaboration networks.
• Compared temporal correlations and topological similarities of events across different network types and orders.

Main Results:

• Physical contact networks exhibit correlations: events close in time are topologically similar, and individuals active in one order are often active in others.
• Collaboration networks lack these temporal-topological correlations, suggesting different interaction dynamics.
• Differences are attributed to the proximity-based nature of physical contacts versus the nature of collaborations.

Conclusions:

• Developed a framework to analyze higher-order events in temporal networks, revealing distinct patterns in physical contact versus collaboration networks.
• Individual behavior in group interactions is consistent across different orders in proximity-based networks.
• Findings offer insights into network dynamics and can inform the development of advanced temporal network models.