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Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community

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Published on: May 31, 2019

A coevolving model based on preferential triadic closure for social media networks.

Menghui Li¹, Hailin Zou, Shuguang Guan

¹Department of Physics, East China Normal University, Shanghai. 200241, P. R. China.

Scientific Reports

|August 28, 2013

Summary

Complex network evolution is driven by local dynamics, specifically the formation of triadic links. A new model based on preferential triadic closure accurately reproduces observed network properties.

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A System for Tracking the Dynamics of Social Preference Behavior in Small Rodents

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Published on: November 21, 2019

Area of Science:

Network Science
Computational Social Science
Complex Systems

Background:

Understanding the dynamical origin of complex networks is essential for network science.
Social media platforms like Flickr and Epinions represent typical complex networks with evolving structures.

Purpose of the Study:

To investigate the role of local dynamics in the evolution of social media networks.
To propose and validate a new model for network evolution driven by local interactions.

Main Methods:

Analysis of temporal data from Flickr and Epinions social networks.
Development of a coevolving dynamical model based on preferential triadic closure.
Numerical experiments to test the model's ability to reproduce empirical observations.

Main Results:

Local dynamical patterns, particularly triadic link formation, significantly influence network evolution.
The proposed preferential triadic closure model successfully replicates global network properties.
Model predictions align qualitatively with empirical data from social media networks.

Conclusions:

Preferential triadic closure is a dominant mechanism driving the evolution of complex social networks.
The coevolving dynamical model offers a robust framework for studying network formation.
Local dynamics play a critical role in shaping the global structure of evolving networks.