Greedy Strategy Influence Maximization Algorithm Based on Seed Candidates
LI Meiling1,2, QIAN Fulan1,2, XU Tao1,2, ZHAO Shu1,2, ZHANG Yanping1,2
1. School of Computer Science and Technology,Anhui University,Hefei 230601; 2. Key Laboratory of Intelligent Computing and Signal Processing,Ministry of Education,Anhui University,Hefei 230601
Abstract The hill-climbing greedy algorithm is not easily extended to large-scale social networks due to its high time complexity.In this paper,it is theoretically analyzed that the node set influence evaluation can be transformed into local probability solution,and thus the algorithm efficiency is significantly improved.The local probability solution function is extend to the greedy algorithm.Based on seed candidates,the greedy influence maximization algorithm and the lazy forward influence maximization algorithm are proposed,respectively. Experiments on four real datasets show that the performance of the proposed algorithms is as high as that of cost-effective lazy forward selection,and the proposed algorithms are superior in running time.
Corresponding Authors:
QIAN Fulan,Ph.D.,associate professor.Her research interests include granular computing,social network and recommendation system.
About author:: LI Meiling,master student.Her research interests include complex network and influence maximization.XU Tao,master student.His research inte-rests include complex network and influence maximization.ZHAO Shu,Ph.D.,professor.Her research interests include granular computing,quotient space theory and machine learning.ZHANG Yanping,Ph.D.,professor.Her research interests include intelligent computing,quotient space theory,machine learning and intelligent information processing.
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2020, Vol. 33 
