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지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 대한전자공학회 IEIE Transactions on Smart Processing & Computing IEIE Transactions on Smart Processing & Computing Vol.13 No.6
- 발행연도
- 2024.12
- 수록면
- 632 - 641 (10page)
- DOI
- 10.5573/IEIESPC.2024.13.6.632
이용수
초록· 키워드
오류제보하기
Online social networks have become a significant medium for disseminating and acquiring information. This paper proposes a modeling method to mine important nodes in social networks using a super adjacency matrix temporal network based on the weakening of interactions between layers and the influence maximization algorithm of a temporal network. The centrality of eigenvectors was introduced to assess the importance of nodes, and the intensity of interlayer coupling was described using an attenuation factor. In addition, the calculation method of the propagation probability between nodes was also defined. The maximum connectivity components of the proposed model on the Enrons dataset were 0.744 and 0.7412 under different circumstances, and the maximum network performance changes were 0.229 and 0.02998. The maximum running times of the influence maximization algorithm under different conditions were 25.656 s and 58.302 s. The research results have practical significance in providing accurate advertising and information dissemination.
목차
Abstract
1. Introduction
2. Related Work
3. Node Ranking and Influence Maximization Algorithm Design based on Interlayer Coupling Strength Attenuation
4. Analysis of Node Ranking and Influence Maximization Algorithm Results based on Interlayer Coupling Strength Attenuation
5. Conclusion
References
참고문헌 (20)
참고문헌 신청
W. Yan, Q. Liu, R. Chen, and M. Zhang, “Favoritism or equality: Difference analysis of users’ utilization of academic social networks for top research corporations,” Online Information Review, Vol. 45, no. 1, pp. 240-260, 2020. Article (CrossRef Link)
G. Anand, S. Srivastava, and A. Shandilya, “Recurrent neural networks in predicting the popularity of online social networks content: A review,” ECS Transactions, Vol. 107, no. 1, pp. 19991-20003, 2022. Article (CrossRef Link)
C. Wang, H. Zhu, and B. Yang, “Composite behavioral modeling for identity theft detection in online social networks,” IEEE Transactions on Computational Social Systems, Vol. 9, no. 2, pp. 428-439, 2022. Article (CrossRef Link)
Y. Liu, J. Wang, H. He, G. Huang, and W. Shi, “Identifying important nodes affecting network security in complex networks,” International Journal of Distributed Sensor Networks, Vol. 17, no. 2, pp. 1560-1571, 2021. Article (CrossRef Link)
Halevy, C. Canton-Ferrer, H. Ma, U. Ozertem, P. Pantel, and M. Saeidi, “Preserving integrity in online social networks,” Communications of the ACM, Vol. 65, no. 2, pp. 92-98, 2022. Article (CrossRef Link)
G. Anand, S. Srivastava, and A. Shandilya, “Recurrent neural networks in predicting the popularity of online social networks content: A review,” ECS Transactions, Vol. 107, no. 1, pp. 19991-20003, 2022. Article (CrossRef Link)
C. Wang, H. Zhu, and B. Yang, “Composite behavioral modeling for identity theft detection in online social networks,” IEEE Transactions on Computational Social Systems, Vol. 9, no. 2, pp. 428-439, 2022. Article (CrossRef Link)
Y. Liu, J. Wang, H. He, G. Huang, and W. Shi, “Identifying important nodes affecting network security in complex networks,” International Journal of Distributed Sensor Networks, Vol. 17, no. 2, pp. 1560-1571, 2021. Article (CrossRef Link)
Halevy, C. Canton-Ferrer, H. Ma, U. Ozertem, P. Pantel, and M. Saeidi, “Preserving integrity in online social networks,” Communications of the ACM, Vol. 65, no. 2, pp. 92-98, 2022. Article (CrossRef Link)
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