A study on the influential neighbors to maximize information diffusion in online social networks

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.