Influence maximization on multiple social networks

Influence Maximization aims to identify k nodes from a network such that the influence spread invoked by the k nodes is maximized. It has various real applications in viral-marketing areas and has been extensively studied by the academic and industrial communities. We observe that existing works all focus on a single network, which identifies k nodes that has the maximal influence spread on one given network; however, with the popularization of social networks, a variety of social platforms are emerged to fulfill various social needs, which leads that social populations will not be confined to a social network, and they will be distributed in different social networks. The direct issue is that the viral-marketing based applications, such as the product promotion on a single network can't meet the breadth demands of current marketing promotion, it's probably that the whole user of the network can't reach the number of targeted population, or the advertisers expect to maximize the influence spread on multiple social networks with k users. Compared with single network, more challenges come forth. It is challengeable to find k users that have the maximal influence spread on the multiple social networks, since it has been proven that influence maximization problem is NP hard. It is more complex to evaluate the influence strength between nodes, since the information propagation is more intricacy among multiple social networks. Besides, entity recognition has to be considered when analyze influence on multiple social networks, since it is normal for a person to have multiple social network accounts. With regards to these, in this paper, we study the influence maximization problem on multiple social networks. In summary, we study the differences of influence maximization between single network and multiple social networks carefully, and propose the self propagation property of entity to build relation among different networks. Later, we propose the influence calculation model to model the influence strength between nodes. We extend the tree-based algorithm model to adapt the multiple social networks situation, based on the proposed influence calculation model and the extended tree-based algorithm model, and we propose multiple optimized strategies to promote performance, such as by further exploring the submodular property to avoid redundant computation, to accelerate seed selection by using the upper influence marginal benefit to approximate the accurate benefit, etc. Finally, numerical experimental studies on real datasets demonstrate the proposed algorithms outperform existing methods significantly, and detailed experimental studies from influence spread and running time have been illustrated respectively. © 2016, Science Press. All right reserved.