An MCDM integrated adaptive simulated annealing approach for influence maximization in social networks
出版年份 2020 全文链接
标题
An MCDM integrated adaptive simulated annealing approach for influence maximization in social networks
作者
关键词
Influence maximization, Simple additive weighting, Adaptive simulated annealing, Social networks, MCDM approach
出版物
INFORMATION SCIENCES
Volume 556, Issue -, Pages 27-48
出版商
Elsevier BV
发表日期
2020-12-29
DOI
10.1016/j.ins.2020.12.048
参考文献
相关参考文献
注意：仅列出部分参考文献，下载原文获取全部文献信息。- A discrete shuffled frog-leaping algorithm to identify influential nodes for influence maximization in social networks
- (2019) Jianxin Tang et al. KNOWLEDGE-BASED SYSTEMS
- TIFIM: A Two-stage Iterative Framework for Influence Maximization in Social Networks
- (2019) Qiang He et al. APPLIED MATHEMATICS AND COMPUTATION
- LAPSO-IM: A learning-based influence maximization approach for social networks
- (2019) Shashank Sheshar Singh et al. APPLIED SOFT COMPUTING
- Identification of influential users in social network using gray wolf optimization algorithm
- (2019) Ahmad Zareie et al. EXPERT SYSTEMS WITH APPLICATIONS
- A hybrid algorithm based on community detection and multi attribute decision making for influence maximization
- (2018) Masoud Jalayer et al. COMPUTERS & INDUSTRIAL ENGINEERING
- Influence maximization in social networks based on TOPSIS
- (2018) Ahmad Zareie et al. EXPERT SYSTEMS WITH APPLICATIONS
- Influence maximization in large social networks: Heuristics, models and parameters
- (2018) Sumith N. et al. Future Generation Computer Systems-The International Journal of eScience
- Influence Maximization on Social Graphs: A Survey
- (2018) Yuchen Li et al. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
- Efficient and effective influence maximization in social networks: A hybrid-approach
- (2018) Yun-Yong Ko et al. INFORMATION SCIENCES
- DDSE: A novel evolutionary algorithm based on degree-descending search strategy for influence maximization in social networks
- (2018) Laizhong Cui et al. JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
- Community-based seeds selection algorithm for location aware influence maximization
- (2018) Xiao Li et al. NEUROCOMPUTING
- Identification of top-k influential nodes based on enhanced discrete particle swarm optimization for influence maximization
- (2018) Jianxin Tang et al. PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
- Positive influence maximization in signed social networks based on simulated annealing
- (2017) Dong Li et al. NEUROCOMPUTING
- Maximizing influence in a social network: Improved results using a genetic algorithm
- (2017) Kaiqi Zhang et al. PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
- An Efficient Memetic Algorithm for Influence Maximization in Social Networks
- (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
- (2016) Peng Wang et al. INFORMATION SCIENCES
- Influence maximization in social networks based on discrete particle swarm optimization
- (2016) Maoguo Gong et al. INFORMATION SCIENCES
- Dynamics of information diffusion and its applications on complex networks
- (2016) Zi-Ke Zhang et al. PHYSICS REPORTS-REVIEW SECTION OF PHYSICS LETTERS
- Locating influential nodes in complex networks
- (2016) Fragkiskos D. Malliaros et al. Scientific Reports
- Information diffusion in online social networks
- (2013) Adrien Guille et al. SIGMOD RECORD
- Reprint of: The anatomy of a large-scale hypertextual web search engine
- (2012) Sergey Brin et al. Computer Networks
- Solving the traveling salesman problem based on an adaptive simulated annealing algorithm with greedy search
- (2011) Xiutang Geng et al. APPLIED SOFT COMPUTING
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