On Measurement of Influence in Social Networks

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.