Spreading and Opinion Dynamics in Social Networks"

Boleslaw Szymanski
Claire and Roland Schmitt Distinguished Professor and the Director of the ARL Social and Cognitive Networks Academic Research Center
Rensselaer Polytechnic Institute
Location: 
Wachman 1015D
Date: 
Tuesday, September 24, 2013 - 14:00
Human behavior is profoundly affected by the influenceability of individuals and their social networks. This talk discusses the dynamics of spread of opinions in such networks using fundamental models for Social Contagion: the binary agreement model (influencing with committed minorities) and threshold model (threshold contact process). In the first one all individuals initially adopt either opinion A or B, and a small fraction of all individuals commits to their opinions. Committed individuals are immune to influence but otherwise follow the prescribed rules for opinion change. We show that the prevailing majority opinion in a population can be rapidly reversed by a small fraction of randomly distributed committed individuals. When committed individuals exist for both opinions, the difference between larger and smaller fractions of them needed for rapid majority conversion decreases as the smaller minority increases. The results are relevant in understanding and influencing the social perceptions of ideas and policies.  
 
We used the threshold model to find efficient spreaders, fast heuristic selection strategies, and impact of clustering on system dynamics. We find that even for arbitrarily high value of threshold, there is a critical initiator fraction beyond which the cascade becomes global. Network structure, in particular clustering, plays a significant role in this scenario. Similarly to the case of single-node or single-clique initiators studied previously, we observe that community structure within the network facilitates opinion spread to a larger extent than a homogeneous random network. Finally, we study the efficacy of different initiator selection strategies on the size of the cascade and the cascade window.