Abstract
The problem of spreading information is a topic of considerable recent interest, but the traditional influence maximization problem is inadequate for a typical viral marketer who cannot access the entire network topology. To fix this flawed assumption that the marketer can control any arbitrary k nodes in a network, we have developed a decentralized version of the influential maximization problem by influencing k neighbors rather than arbitrary users in the entire network. We present several practical strategies and evaluate their performance with a real dataset collected from Twitter during the 2010 UK election campaign. Our experimental results show that information can be efficiently propagated in online social networks using neighbors with a high propagation rate rather than those with a high number of neighbors. To examine the importance of using real propagation rates, we additionally performed an experiment under the same conditions except the use of synthetic propagation rates, which is widely used in studying the influence maximization problem and found that their results were significantly different from real-world experiences.
Highlights
In the field of social network analysis, a fundamental problem is to develop an epidemiological model for finding an efficient way to spread information through the model
In the Independent Cascade (IC) model proposed by Goldenberg et al [2], (1) some non-empty set of nodes are initially activated; (2) at each successive step, the influence is propagated by activated nodes, independently activating their inactive neighbors based on the propagation probabilities of the adjacent edges
Experimental results we analyze the performance of the selection schemes presented in Section ‘Neighbor selection schemes.’
Summary
In the field of social network analysis, a fundamental problem is to develop an epidemiological model for finding an efficient way to spread information through the model. It seems natural that many people are often influenced by their friends’ opinions or recommendations. This is called the ‘word of mouth’ effect and has for long been recognized as a powerful force affecting product recommendation [1]. Recent advances in the network theory have provided us with the mathematical and computational tools to understand them better. Activated nodes mean the nodes that have adopted the information or have been infected. This models how a piece of information
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