Influence maximization based on node attraction model
Author(s)
Wang, G
Jiang, J
Li, W
Wang, C
Griffith University Author(s)
Year published
2019
Metadata
Show full item recordAbstract
In the research of the problem of maximizing the influence of social networks, a propagation model based on the attraction between nodes is proposed for the calculation of node information propagation influence. In this study, we model the node properties of the network and use it to define the direct attraction and cumulative attraction, in which we consider the change and attenuation of the node influence. Also, we discuss the gravitational features of the nodes to establish a propagation method based on the independent cascade model. The probability of the node attraction is designed for activation between nodes. Finally, ...
View more >In the research of the problem of maximizing the influence of social networks, a propagation model based on the attraction between nodes is proposed for the calculation of node information propagation influence. In this study, we model the node properties of the network and use it to define the direct attraction and cumulative attraction, in which we consider the change and attenuation of the node influence. Also, we discuss the gravitational features of the nodes to establish a propagation method based on the independent cascade model. The probability of the node attraction is designed for activation between nodes. Finally, experiments on actual data sets show that the influence under this method is better than other methods.
View less >
View more >In the research of the problem of maximizing the influence of social networks, a propagation model based on the attraction between nodes is proposed for the calculation of node information propagation influence. In this study, we model the node properties of the network and use it to define the direct attraction and cumulative attraction, in which we consider the change and attenuation of the node influence. Also, we discuss the gravitational features of the nodes to establish a propagation method based on the independent cascade model. The probability of the node attraction is designed for activation between nodes. Finally, experiments on actual data sets show that the influence under this method is better than other methods.
View less >
Conference Title
Proceedings - IEEE 17th International Conference on Dependable, Autonomic and Secure Computing, IEEE 17th International Conference on Pervasive Intelligence and Computing, IEEE 5th International Conference on Cloud and Big Data Computing, 4th Cyber Science and Technology Congress, DASC-PiCom-CBDCom-CyberSciTech 2019
Subject
Distributed computing and systems software