Fastest Mixing Markov Chain on a Graph
SIAM Review
Graph evolution: Densification and shrinking diameters
ACM Transactions on Knowledge Discovery from Data (TKDD)
Distributed average consensus with least-mean-square deviation
Journal of Parallel and Distributed Computing
Information flow modeling based on diffusion rate for prediction and ranking
Proceedings of the 16th international conference on World Wide Web
The structure of information pathways in a social communication network
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
User interactions in social networks and their implications
Proceedings of the 4th ACM European conference on Computer systems
On the evolution of user interaction in Facebook
Proceedings of the 2nd ACM workshop on Online social networks
Randomized consensus algorithms over large scale networks
IEEE Journal on Selected Areas in Communications
Data-driven modeling and analysis of online social networks
WAIM'11 Proceedings of the 12th international conference on Web-age information management
I act, therefore I judge: network sentiment dynamics based on user activity change
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
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Online social networks provide a globally available, massive-scale infrastructure for people to exchange information and ideas. A topic of great interest in social networks research is how to model this information exchange and, in particular, how to model and analyze the effects of interpersonal influence on processes such as information diffusion, influence propagation, and opinion formation. Recent empirical studies indicate that, in order to accurately model communication in online social networks, it is important to consider not just relationships between individuals, but also the frequency with which these individuals interact. We study a model of opinion formation in social networks proposed by De Groot and Lehrer and show how this model can be extended to include interaction frequency. We prove that, for the purposes of analysis and design, the opinion formation process with probabilistic interactions can be accurately approximated by a deterministic system where edge weights are adjusted for the probability of interaction. We also present simulations that illustrate the effects of different interaction frequencies on the opinion dynamics using real-world social network graphs.