Abstract
Rising stars are junior individuals in social networks who will have high impacts with time accumulation. In this paper, we study the problem of rising star evaluation in geo-social networks. Specifically, given a topic keyword, we aim at evaluating the latent influence of users to find rising star, which refer to expert that may have few activities and little impact currently in the underlying geo-social network, but he or she will become influential experts in the future. Most of the current research studies focus on experts finding rather than rising stars. Rising stars can bring great opportunities. We investigate a novel processing framework based on extreme learning machine (ELM) called FS-ELM to efficiently evaluate future stars. Our experimental studies conducted on real-world datasets demonstrate that our method is efficient in predicting rising star with potential impact.
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Acknowledgments
This research is partially funded by the National Natural Science Foundation of China under Grant Nos. 61572119, 61622202, 61732003, 61729201, 61702086, and U1401256, and the Fundamental Research Funds for the Central Universities under Grant Nos. N150402005, and N171904007.
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Ma, Y., Yuan, Y., Wang, G., Bi, X., Wang, Z., Wang, Y. (2020). Rising Star Classification Based on Extreme Learning Machine. In: Cao, J., Vong, C., Miche, Y., Lendasse, A. (eds) Proceedings of ELM 2018. ELM 2018. Proceedings in Adaptation, Learning and Optimization, vol 11. Springer, Cham. https://doi.org/10.1007/978-3-030-23307-5_22
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DOI: https://doi.org/10.1007/978-3-030-23307-5_22
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