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Cyber group event is one of the vital objects of cyber information content management. The study of user influence in cyber group events is of great significance to the analysis of user responsibility and the management of cyberspace. Aiming at the problem that the user influence rank algorithms existing in current research do not apply to cyber group events, this paper proposes a user influence rank algorithm based on interaction behaviors (IB-UIR) to measure users' influence in cyber group events. Based on PageRank, the IB-UIR algorithm comprehensively considers users' behaviors and the time interval between behaviors and calculates user influence iteratively in the user interaction network. Based on the real Sina Weibo data, experimental results show that the IB-UIR algorithm can effectively calculate users' actual influence in cyber group events, and it outperforms other related algorithms in the coverage, precision, recall, and F1-measure.

This paper focuses on establishing the electricity consumption-forecasting model based on the Gene Expression Programming that was brought forward by a Portuguese researcher, named Ferreira C. in 2001. The result demonstrates that the methodology that can overcome barriers such as: limited data, complex calculating process, is feasible and effective with higher accuracy