Identification of Active Valuable Nodes in Temporal Online Social Network with Attributes

The rapidly growing online social networks have generated great expectations connected with their potential business values. The aim of this paper is to identify the active valuable nodes that can spread business information to a large fraction of the individuals in large-scale temporal online social networks as quickly as possible. Most studies focus on static social networks, the study on the identification of active valuable nodes in temporal online social networks with quantitative attributes is still young. In this paper, we propose a method to identify active valuable nodes based on their static structural properties and temporal behavioral attributes. The method first chooses the candidates of the active valuable nodes by the static analysis of their structural properties. Then, the candidate's behavioral trend is extracted from its activity records. Through analyzing the spatio-temporal characteristics of the behavioral trend, the method distinguishes active valuable nodes from inactive ones and reveals typical evolutionary processes. We perform experiments on two practical online social networks with thousands of nodes. The experimental results demonstrate that the method can identify the active valuable nodes for information diffusion in large-scale temporal online social networks accurately and efficiently. It would be useful for business applications.