2014 International Conference on Cloud Computing and Big Data (CCBD) (2014)
Nov. 12, 2014 to Nov. 14, 2014
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/CCBD.2014.34
Social network influence maximization problem aims to design algorithms that can maximize the scope of the nodes affected by the specified nodes set. This paper studies and improves existing algorithms by the introduction of hidden influence and floating influence. Experiments show that the HGA algorithm works more effectively.
Social network services, Data mining, Algorithm design and analysis, Integrated circuit modeling, Greedy algorithms, Knowledge discovery, Heuristic algorithms
W. Zeng, C. Li, B. Feng, N. Yang and Q. Luo, "Discovering Most Influential Social Networks Nodes Based on Floating Influence," 2014 International Conference on Cloud Computing and Big Data (CCBD), Wuhan, China, 2014, pp. 144-147.