Adhesion-Induced Discontinuous Transitions and Classifying Social Networks

Area of Science:

• Complex Systems Science
• Network Science
• Statistical Physics

Background:

• Complex systems exhibit qualitative changes at transition points.
• Explosive transitions have been observed in network growth, but not common restructuring processes.
• Real networks undergo both growth and restructuring.

Purpose of the Study:

• To uncover intrinsically discontinuous transitions in nongrowing networks.
• To investigate network restructuring processes controlled by adhesion.
• To differentiate network phases based on degree distributions.

Main Methods:

• Derivation of a master equation for temporal network evolution.
• Analytic solution of the master equation.
• Analysis of empirical data from abstract and face-to-face social networks.

Main Results:

• Identification of genuinely discontinuous transitions in nongrowing networks.
• Separation of network phases with monotonic and peaked degree distributions.
• Empirical data analysis reveals similar distribution patterns in social networks.

Conclusions:

• Adhesion-controlled network restructuring can lead to discontinuous phase transitions.
• Degree distribution patterns can distinguish between abstract and face-to-face social networks.
• The study reveals fundamental differences in network dynamics based on restructuring mechanisms.