Budgeted influence and earned benefit maximization with tags in social networks
Author:
Publisher
Springer Science and Business Media LLC
Subject
Computer Science Applications,Human-Computer Interaction,Media Technology,Communication,Information Systems
Link
https://link.springer.com/content/pdf/10.1007/s13278-021-00850-z.pdf
Reference34 articles.
1. Banerjee S, Jenamani M, Pratihar DK (2019a) Combim: a community-based solution approach for the budgeted influence maximization problem. Expert Syst Appl
2. Banerjee S, Jenamani M, Pratihar DK (2019b) Maximizing the earned benefit in an incentivized social networking environment: a community-based approach. J Ambient Intell Hum Comput: 1–17
3. Banerjee S, Jenamani M, Pratihar DK (2020) Maximizing the earned benefit in an incentivized social networking environment: a community-based approach. J Ambient Intell Hum Comput 11(6):2539–2555
4. Banerjee S, Jenamani M, Pratihar DK (2020b) A survey on influence maximization in a social network. Knowl Inf Syst: 1–39
5. Banerjee S, Pal B, Jenamani M (2020c) Budgeted influence maximization with tags in social networks. In: International conference on web information systems engineering. Springer, pp 141–152
Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. Influence Maximization with Tag Revisited: Exploiting the Bi-submodularity of the Tag-Based Influence Function;Advanced Data Mining and Applications;2023
2. AMIR: A Multi-agent Approach for Influence Detection in Social Networks;Lecture Notes in Networks and Systems;2023
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