Harshit Srivastava
ABSTRACT
Cooperation or Cooperative behavior constrained between any two nodes or groups always result in constant scrutiny for reconfiguration. This continual reconfiguration creates a new modulus for expansion and thus detecting community structure can fundamentally become a problem of identifying groups and a leader in a network. In a network, the influencer is commonly termed as leader and the leader node is a node that has high attraction to increase, i.e., high degree of centrality. In this paper, we devised an efficient method to detect influencers in a network through cooperative and spread strategies. This dynamic strategy technique is used to detect subevents and anomalies through social and physical sensor data. This paper contributes toward a dynamic game theory approach for information maximization by maximizing the influence features over the network for higher information delivery over the dynamic network.
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Digital Library
- Riou, Matthieu, Bassam Jabaian, Stéphane Huet, and Fabrice Lefèvre. "Reinforcement adaptation of an attention-based neural natural language generator for spoken dialogue systems." Dialogue & Discourse 10, no. 1 (2019): 1-19. DOI: https:// doi,.org/10.5087/dad.2019.101Google ScholarCross Ref
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- H. Srivastava and R. Sankar, "Information Dissemination from Social Network for Extreme Weather Scenario," in IEEE Transactions on Computational Social Systems, vol. 7, no. 2, pp. 319-328, April 2020. DOI: https://doi.org/10.1109/TCSS.2020.2964253.Google ScholarCross Ref
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- Bhattacharyya, Samit, and Chris T. Bauch. "“Wait and see” Vaccinating Behaviour During a Pandemic: A Game Theoretic Analysis." Vaccine 29, no. 33 (2011): 5519-5525. I. S. Jacobs and C. P. Bean. DOI: https://doi.org/10.1016/j.vaccine.2011.05.028Google Scholar
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- Velez, John A. "Extending the theory of Bounded Generalized Reciprocity: An Explanation of The Social Benefits of Cooperative Video Game Play." In Computers in Human Behavior 48 (2015): 481-491. DOI: https://doi.org/10.1016/j.chb.2015.02.015 Google ScholarDigital Library
- Lin, Su-Chen, Shou-De Lin, and Ming-Syan Chen. "A Learning-Based Framework to Handle Multi-Round Multi-Party Influence Maximization on Social Networks." In Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 695-704. 2015. DOI: https://doi.org/10.1145/2783258.2783392 Google ScholarDigital Library
Index Terms
- Cooperative Influence Learning
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