With Time Constraints And Subject-oriented Impact Maximization Studies

With the development of Internet technology and the popularity of computers and smart phones, such as Facebook, Twitter and other social networking sites were a great success, which is a sharp increase of the users. The influence of Internet on people’s lives is more and more important and generated the Network Marketing to sell the products. The Network Marketing is based on the personal relationship of the online social network, such as friendship, colleagues or concerned relationship,which the influence spreading based on the "Word-of-Mouth" effect.Against this background,the influence maximization problem in the online social networks attracted widespread attention. The Influence Maximization problem is to find the seed set of K. nodes in social networks. Making the benefit of the influence spreading is the maximum in the final.For influence maximization problem have been many research results. For example, using the greedy algorithm to deal with the influence maximization problem, and the expansion algorithm based on Linear Threshold Model and Independent Cascade Model. However, these algorithms are constantly improved by the efficiency and effectiveness of the algorithm. But there is no one concerned the influence maximization problem both the time constraint and the subject of the spreading information. In fact, the spreading information from the networks has a subject feature, and the time constraints. So we need to research the influence maximization problem with time constraints and subject.In this paper,we propose a new influence maximization problem with time constrained and subject-oriented based on the traditional. We consider a establishment model with time constrained and subject named LAICS(Latency Aware Independent Cascade based on Subject). In this model, we considering the time constraints and subject, building a user preference model. With this model, to calculate the probability that the user activated by the information. What’s more, we consider the time delay of the user in the influence spreading process. We propose the TGAS(the Time constrained Greedy Algorithm based on Subject) algorithm to solve the influence maximization problem with the specific information. This algorithm is based on the framework of greedy algorithm, it considered the user’s preferences and the time delay from the information dissemination process. And,we redesign the influence calculation method. The result based on TGAS has a higher accuracy, and it is better to adapt of the product promotions with time constraint information.