An MCDM integrated adaptive simulated annealing approach for influence maximization in social networks
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Title
An MCDM integrated adaptive simulated annealing approach for influence maximization in social networks
Authors
Keywords
Influence maximization, Simple additive weighting, Adaptive simulated annealing, Social networks, MCDM approach
Journal
INFORMATION SCIENCES
Volume 556, Issue -, Pages 27-48
Publisher
Elsevier BV
Online
2020-12-29
DOI
10.1016/j.ins.2020.12.048
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- (2017) Kaiqi Zhang et al. PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
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- (2016) Maoguo Gong et al. IEEE Computational Intelligence Magazine
- A novel hybrid MCDM model combining the SAW, TOPSIS and GRA methods based on experimental design
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- (2016) Fragkiskos D. Malliaros et al. Scientific Reports
- Information diffusion in online social networks
- (2013) Adrien Guille et al. SIGMOD RECORD
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- (2012) Sergey Brin et al. Computer Networks
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