Influence Maximization In Social Networks Based On Competition

In social networks, many different oponions or products are competing with one another for their influence. Such as a company wants to intruduce a new product into a market where a competing product is already being introducted. And consumers will use only one of the two products and will influence their friends in their decision of which product to use.However, the research of traditional influence maximization problem only considered the dissemination of one information. In fact, there are multiple pieces of information spreading on network, how to get the best marketing effect in this condition needs to reconsider. Based on this, this thesis puts forward the problem of influence maximization based on competition.To solve this problem, this thesis gives the definition of Competition Based Liner Threshold Model. Under the model, this thesis focus on the problem that one message tries to spread its influence propagation as much as possible by strategically selecting anumber of seed nodes when seed nodes of its competing message are already known. And calculate the influence probability between two nodes. This thesis combines the influence between information and the feature of propagation model, and proposes a algorithm based on the subgraph of node, then proposes the improved algorithm. Based on the research, a prototype system is designed and implementationed.In this thesis, experiments were carried out on the dataset of Weibo and Competiton Based Liner Threshold Model. The results show that:(a)in the range of influence, the proposed algorithm is superior to the traditional heuristic algorithms; (b) in the time complexity, although the running time is longer than that of the heuristic algorithm, but it is within acceptable range. The results of the experiments verify the effective of the algorithm.