Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Mining knowledge-sharing sites for viral marketing
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
The Volume of the Giant Component of a Random Graph with Given Expected Degrees
SIAM Journal on Discrete Mathematics
The dynamics of viral marketing
ACM Transactions on the Web (TWEB)
Epidemic thresholds in real networks
ACM Transactions on Information and System Security (TISSEC)
Competitive influence maximization in social networks
WINE'07 Proceedings of the 3rd international conference on Internet and network economics
Networks, Crowds, and Markets: Reasoning About a Highly Connected World
Networks, Crowds, and Markets: Reasoning About a Highly Connected World
Opinion formation under costly expression
ACM Transactions on Intelligent Systems and Technology (TIST)
Finding spread blockers in dynamic networks
SNAKDD'08 Proceedings of the Second international conference on Advances in social network mining and analysis
Finding critical nodes for inhibiting diffusion of complex contagions in social networks
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part II
A note on maximizing the spread of influence in social networks
Information Processing Letters
Controlling opinion bias in online social networks
Proceedings of the 3rd Annual ACM Web Science Conference
Opinion Fluctuations and Disagreement in Social Networks
Mathematics of Operations Research
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Motivated by applications such as the spread of ideologies and political views, we study opinion dynamics in online networks under voter models. It is well known that the binary version of these models, where the state (or opinion) of each agent is 0 or 1, always leads to consensus. We consider an extension, in which some nodes are ''stubborn'', i.e., do not change their states based on other nodes. In such a system, the asymptotic average opinion could be between 0 and 1. The goal of this paper is to study the ease with which bias (i.e., the tendency of the opinion to become close to 0) can be controlled (so that the average opinion exceeds a specified threshold). We formalize a new parameter, called the Minimum Opinion Control Factor (MOCF), to capture this, and study it through analysis and simulations on real online and synthetic networks. Finally, we experimentally demonstrate the usefulness of combining the voter model with an independent cascade model in controlling bias and we explain these findings in terms of network structure.