Controllable Information Spreading Based On Influence In Mobile Social Networks

With the popularization of mobile devices and the mature development of Internet technology,mobile social networks emerge as the times require.The user uses the mobile terminal as a medium to combine the real relationship with the virtual network to conduct social activities.High influence users can accurately grasp the traits of information spreading in the network,and use their high influence to trigger the remaining users to participate in information spreading as much as possible so that the spreading speed of information is fast and the range is wide.However,when people spread information,they often have their subjective consciousness.The information spreading in the mobile social network may be positive energy information or malicious false information.Therefore,this thesis pays attention to the role of influence on the controllable spreading of information in mobile social networks:on the one hand,users with high social influence can guide individuals to effectively predict and control the law of information spreading,which makes positive information spreading fast and wide.On the other hand,influential users with credibility can find false information in time and suppress the spread of false information by sending correct information through the information competition model.When the number of users who spread false information converges,it means that the spread of false information is controllable.Therefore,this thesis focuses on controllable information spreading based on influence in mobile social networks,to promote positive information spreading and suppress malicious information spreading.The following research work has been carried out:Firstly,aiming at the sociality and mobility of mobile social networks,this thesis proposes the information maximization scheme based on social relations and the information maximization scheme based on fluctuating particle swarm optimization.We use the particle swarm motion process to describe the stochasticity of the user information spreading process,and identify the most influential users in the network through the interaction characteristics of users.Then we propose a novel population update algorithm at the optimization stage to save the problem of maximizing information spreading,called the FPSO algorithm,which,to the best of our knowledge,is the first attempt of considering the sudden changes in communication relationships in the stochastic process of influence propagation in MSNs.The scheme improves the accuracy of identifying the most influential users and better realize the maximization of information spreading.Secondly,because of the high aggregation of mobile social networks,this thesis proposes an information maximization scheme based on network motifs in mobile social networks.We focus on Network Motifs(NM)as drivers of influence to impact the spreading process,we propose IM-NM,a network motifs-based influence maximation scheme for delivering information efficiently.In consideration of the communication relationship and the users’ attributes,we first define Weight Ratio(WR),Degree Density(DD),and Structural Stability Level(SSL).Then we identify the key network motifs by Naive Bayesian machine learning.Finally,we adopt the key network motifs as the unit structure to reconstruct the network to maximize the information.We implement our proposed methods on a set of real-world networks to evaluate the performance,the experimental results demonstrate that our proposal achieves better performance than other related methods.Thirdly,according to the characteristics of opinion leaders in mobile social networks,this thesis proposes proactive rumors control scheme with positive communication dominating sets in mobile social networks.Firstly,we further consider the communication relates to develop positive communication dominating sets to control rumors,the dominators can guide the tendency of public opinion.Then,we design a competitive information spreading model named Independent Cascade with Beacon Model(IC-B),the model is an independent cascade with a beacon model,when malicious information appears,beacon users can send the correct information to control the spreading of information in time.Finally,we design the rumor control algorithm and experimental results on five real-world datasets with various scales to evaluate the performance that have shown the superiority of the proposed scheme.Finally,pointing at the characteristic that a mobile social network is a crossnetwork composed of a mobile communication network and a social network,this thesis proposes an information spreading control method in mobile social networks based on network motifs.Firstly,a Multientity Competitive Independent Cascade(MCIC)model in the social network layer is established.Secondly,this thesis defines the Control Information Flow Motif(CIFM),determines the key network motifs,and designs its efficient and controllable spreading algorithm in the communication layer.Finally,theoretical derivation proves that this method has convergence,and the simulation results show that our method not only has more advantages in terms of time efficiency but also has the best effect in controlling information spreading.