Research Of Influence Maximization Algorithms In Social Networks

The development of Internet technology has been gradually changing people's life, the rise of social networks makes the connections between people more convenient. At the same time,some businesses take advantage of "word-of-mouth" of celebrities to make their products well known,this is the influence maximization problem. Influence maximization problem is first proposed by Domingos,which has become a hot research topic in the field of social networks. Scholars combined the theory of social network analysis, the transmission of all kinds of social network model were established according to the propagation rules of real network. In addition, the various combination of propagation model influence maximization algorithms were proposed, which be used to solve the influence maximization problem in social networks.In this paper,we propagation a parallel search community influence maximization and an improved propagation model based on the independent cascade model. This paper studies the following question:(1)The parallel search community influence maximization algorithm is proposed to solve the problems of social networks influence maximization algorithm,and we build a Hadoop environment to do the experiments. The final results show that the algorithm can be applied to large scale social network. Search the most influential nodes can expand influence spread range in the community. The search for the realization of parallel which could improve the performance of running the algorithm; (2) We propose an improved propagation model based on the independent cascade model. The model constantly revises the activation probability in the information dissemination process to make it more in line with the actual communication process and it is proved by experiments that results of the propagation in this model is more in line with the reality results.