A Study on the Node Centrality Based Multi-social Attributes Weighted in Mobile Social Networks

Mobile social networks (MSNs) exploit human mobility and consequent device-to-device contact to opportunistically realize data communication. Thus links in MSNs is dynamic changing over time and strongly influenced by people activities, mining influential nodes is one of the important questions for effective information transmission in MSNs. While traditional centrality definitions are based on the static binary network model and not suitable for time-varying topology structure in mobile social network. Furthermore previous centrality metrics often referred to social attributes about neighbor nodes and contact times, and did not take the contact duration time into consideration. Therefore, this paper proposes a centrality measurement method based on multi-social attributes weighted. We first use the temporal evolution graph model which more accurately depicts the dynamic nature of topology in MSNs. Quantifying human social relations and mobility model as weights for the links, and then we redefine degree of centrality and the measurement of shortest path. Finally, the superiority of the concepts we posed are evaluated in the real data set.