Rumor Diffusion Model Based on Representation Learning and Anti-Rumor

Article Properties
Cite
Xiao, Yunpeng, et al. “Rumor Diffusion Model Based on Representation Learning and Anti-Rumor”. IEEE Transactions on Network and Service Management, vol. 17, no. 3, 2020, pp. 1910-23, https://doi.org/10.1109/tnsm.2020.2994141.
Xiao, Y., Yang, Q., Sang, C., & Liu, Y. (2020). Rumor Diffusion Model Based on Representation Learning and Anti-Rumor. IEEE Transactions on Network and Service Management, 17(3), 1910-1923. https://doi.org/10.1109/tnsm.2020.2994141
Xiao, Yunpeng, Qiufan Yang, Chunyan Sang, and Yanbing Liu. “Rumor Diffusion Model Based on Representation Learning and Anti-Rumor”. IEEE Transactions on Network and Service Management 17, no. 3 (2020): 1910-23. https://doi.org/10.1109/tnsm.2020.2994141.
1.
Xiao Y, Yang Q, Sang C, Liu Y. Rumor Diffusion Model Based on Representation Learning and Anti-Rumor. IEEE Transactions on Network and Service Management. 2020;17(3):1910-23.
Refrences
Title Journal Journal Categories Citations Publication Date
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Citations Analysis
The category Science: Mathematics: Instruments and machines: Electronic computers. Computer science 2 is the most commonly referenced area in studies that cite this article. The first research to cite this article was titled Continuous Similarity Learning with Shared Neural Semantic Representation for Joint Event Detection and Evolution and was published in 2020. The most recent citation comes from a 2022 study titled A Novel Tripartite Evolutionary Game Model for Misinformation Propagation in Social Networks. This article reached its peak citation in 2022, with 1 citations. It has been cited in 3 different journals, 33% of which are open access. Among related journals, the Security and Communication Networks cited this research the most, with 1 citations. The chart below illustrates the annual citation trends for this article.
Citations used this article by year