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A Temporal-Causal Modelling Approach to Integrated Contagion and Network Change in Social Networks
Romy Blankendaal, Sarah Parinussa, Jan Treur
1388 - 1396
10.3233/978-1-61499-672-9-1388
Frontiers in Artificial Intelligence and Applications
Volume 285: ECAI 2016
Abstract

This paper introduces an integrated adaptive temporal-causal network model for dynamics in networks of social interactions addressing contagion between states, and changing connections within these social networks by two principles: the homophily principle and the more-becomes-more principle. The model has been evaluated in three different manners: by simulation experiments, by verification based on mathematical analysis, and by validation against an empirical data set.

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