A Novel Influence Maximization Algorithm for a Competitive Environment Based on Social Media Data Analytics

Online social networks are increasingly connecting people around the world.Influence maximization is a key area of research in online social networks,which identifies influential users during information dissemination.Most of the existing influence maximization methods only consider the transmission of a single channel,but real-world networks mostly include multiple channels of information transmission with competitive relationships.The problem of influence maximization in an environment involves selecting the seed node set for certain competitive information,so that it can avoid the influence of other information,and ultimately affect the largest set of nodes in the network.In this paper,the influence calculation of nodes is achieved according to the local community discovery algorithm,which is based on community dispersion and the characteristics of dynamic community structure.Furthermore,considering two various competitive information dissemination cases as an example,a solution is designed for self-interested information based on the assumption that the seed node set of competitive information is known,and a novel influence maximization algorithm of node avoidance based on user interest is proposed.Experiments conducted based on real-world Twitter dataset demonstrates the efficiency of our proposed algorithm in terms of accuracy and time against notable influence maximization algorithms.

Intermittent Geocast Routing in Urban Vehicular Delay Tolerant Networks

Nowadays, both vehicular active safety service and user infotainment service have become two core applications for urban Vehicular Delay Tolerant Networks（u VDTNs）. Both core applications require a high data transmission capacity over u VDTNs. In addition, the connection between any two vehicles in u VDTNs is intermittent and opportunistic. Intermittent data dissemination over u VDTNs is a stringent and challenging issue. In this paper,we propose Intermittent Geocast Routing（IGR）. For the first step, IGR has to estimate the active connection time interval via the moving directions and velocities between any two vehicles. Second, the throughput function for u VDTNs is fitted by building a wavelet neural network traffic model. Third, the throughput function within the effective connection time interval is integrated to obtain the forwarding capability estimation of the node. Fourth, a high-efficiency geocast routing algorithm using the node forwarding capability for u VDTNs is designed. Finally, IGR is simulated on the opportunistic Network Environment simulator. Experimental results show that IGR can greatly improve the packet delivery ratio, transmission delay, delay jitter, and packet loss rate compared with the state of the art.