Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Inferring networks of diffusion and influence
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Discovering Influence in Communication Networks Using Dynamic Graph Analysis
SOCIALCOM '10 Proceedings of the 2010 IEEE Second International Conference on Social Computing
Generating predictive movie recommendations from trust in social networks
iTrust'06 Proceedings of the 4th international conference on Trust Management
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One of the issues to be resolved in social recommender systems is the identification of opinion leaders in a network. Finding effective people in societies has been a key question for many groups, e.g., marketers. The research undertaken in this paper focuses on finding important nodes in a network based on their behaviour as well as the structure of the network. This paper views the propagation of information in a social network as a process of infection. The paper proposes an algorithm called the Probability Propagation Method for measuring the probability of infection of all the nodes in a network starting from a given node in the network. Then, assuming independence in activation of nodes in a network, a method is proposed for ranking nodes according to their capabilities in infecting a larger number of nodes in a network. These methods are validated using simulation software in which a non-deterministic model of information diffusion is simulated on several classes of network.