An Adaptive Computational Network Model for Multi-Emotional Social Interaction

Ramona Roller, Suzan Q. Blommestijn, J. Treur

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review


The study reported in this paper investigates an adaptive temporal-causal
network-model for emotion contagion. The dynamic network principles of emotion contagion and the adaptive principles of homophily and Hebbian learning were used to simulate the change in multiple emotions and social interactions over time. It is shown that the model can be successfully initialised with Twitter data, while parameters were optimised via simulated annealing. Moreover, an exploratory analysis for model validation and applications provided insights in the model's potentials and limitations. The study advances the existing methodology of modelling the social contagion of multiple emotions in a context where also the social network evolves over time.
Original languageEnglish
Title of host publicationProc. of the 6th International Conference on Complex Networks and their Applications
PublisherSpringer Verlag
Publication statusPublished - Nov 2017

Publication series

Name Studies in Computational Intelligence
PublisherSpringer Publishers


Dive into the research topics of 'An Adaptive Computational Network Model for Multi-Emotional Social Interaction'. Together they form a unique fingerprint.

Cite this