Research Of Maximizing Social Network Influence

With the development of mobile edge computing network technology and mobile intelligent devices,the Internet economy is developing rapidly.The types of social networks which make information dissemination more convenient and faster and contact costs saved are increased by the emergence of various social networking sites and communication tools making.The form of human communication has gradually shifted from offline activities to online,social networks have gradually developed,and online sales platforms have also been born.Influenced by marketing strategies such as "viral marketing" and "word of mouth effect",the problem of Influence Maximization(IM)has gradually evolved.This problem focuses on how to select the most influential users to spread information with the widest influence through the propagation model.In the era of the internet of everything,the essence of information dissemination is to carry out migration calculation based on tasks.The optimization problem of migration path selection is derived from the problem of task migration.This problem studies how to select the optimal path of task migration,so as to minimize the delay and energy consumption of information transmission,and spread the most information in the limited energy of mobile intelligent devices,which is the same optimization goal as maximizing the information transmission range in the problem of impact maximization.Through the research work on the problem of maximizing the influence of existing social networks,it is found that most of the existing work is concentrated on the spread of messages in social networks,that is,online publishing of messages;social network information dissemination,that is,the substantive task migration of information in the communication process,has not been considered in depth.For the above two aspects,the main research work of this paper is as follows:For the message propagation in the social network,most of the propagation models are static propagation models.Even though there are a few studies on dynamic propagation models,the important factor of trust is ignored.However,trust relationship plays a crucial role in the process of information dissemination.Introduced based on the above two points,to activate the node number in the transmission of Dynamic change and the Trust relationship between the nodes to improve the Independent Cascade model,combined with the similarity between Social influence and Coulomb force,put forward a kind of Dynamic Social Coulomb force based on Trust relationship(Dynamic Social Coulomb Forces-based on Trust Relationships,DSC-TR)model,build a kind ofRandom optimization Greedy(Random Greedy,RG-DPIM)algorithm to solve the influence maximization problem.The simulation results show that the DSC-TR model has high prediction accuracy.RG-DPIM algorithm can solve the problem of influence maximization more efficiently when the number of active nodes is almost the same as the greedy algorithm.For the spread of information tasks in social networks,most task migration path selection optimization algorithms only consider single energy consumption or time delay as the optimization index.Some researches take joint time delay and energy consumption as the optimization target,and do not apply the influence maximization method to solve the problem.Based on this,the task migration path selection is transformed into the problem of influence maximization in the social network,and the task migration path optimization selection algorithm is constructed.Its core idea is to Edge Server(Edge Server,ES)analogy for the social network nodes,through the K-shell method definition Edge Server path Influence,K-shell is put forward with the combination of greed and heuristic algorithm thought Influence Maximization Task Migration(K-shell Influence Maximization of Task Migration,Ks-IMTM)algorithm,thus effectively reducing energy consumption and delay,and improves the quality of the user experience.