Rising Star Evaluation in Heterogeneous Social Network

Rising stars are junior individuals in the social network who will have high impacts with time accumulation. Rising star evaluation has become a research hotspot in network analysis area recently, which is helpful for decision support, resource allocation, and other practical problems. As a traditional social network, academic social network is stressed because of its heterogeneity and regular data structure. In this paper, we assume there are inside factors influencing individuals behaviors. We process the network parameters and mine inner factors via factor analysis, and train a decision tree to evaluate future impact. Experiment is processed on america physics society dataset, and the result shows our method has better performance than state-of-the-arts.