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Social Similarity Routing Algorithm based on Socially Aware Networks in the Big Data Environment

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Abstract

In the big data environment, the social information of a large number of nodes cannot be reasonably analyzed and utilized, thus leading to the problem of uneven routing performance. Therefore, this paper proposes a Social Similarity Routing Algorithm (SRRA) based on socially aware networks in the big data environment. In the SRRA algorithm, two main parts in which the process of nodes forwarding messages are in-community and out-of-community. First, we defined three indexes for nodes and communities in which nodes are located by analyzing human social behavior: community connectedness between communities, the activity of nodes, and the social similarity of nodes. Then these three indexes are used to make up two measures: the in-community forwarding measure and the out-of-community forwarding measure. When messages are forwarded within a community, we choose nodes with high in-community forwarding measures as relay nodes so that messages can be delivered quickly in the same community. The relay node with the highest out-of-community forwarding measure is chosen to forward the message to the adjacent communities that are as near as possible to the destination community as much as possible when messages are forwarded outside the community, which ensures that messages can always be sent to the target community fast and accurately. The results of the simulation experiments compared with existing routing algorithms prove that the SRRA routing algorithm significantly improves the message delivery ratio while effectively reducing the network overhead and average latency.

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Data available on request from the authors.

Code Availability

Code available on request from the authors.

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Acknowledgements

This work was partially supported by the National Natural Science Foundation of China (No.61972136), and Hubei Natural Science Foundation (No.2020CFB497, No.2020CFB571), MOE(Ministry of Education in China) Project of Humanities and Social Sciences (No.20YJAZH112), the Hubei Provincial Department of Education Outstanding Youth Scientific Innovation Team Support Foundation (No.T201410, T2020017), the Science and Technology Research Projects of Hubei Provincial Department of Education (No.Q20162706).

Funding

This article was partially supported by the National Natural Science Foundation of China (No.61972136) and Hubei Natural Science Foundation (No.2020CFB497, No.2020CFB571), MOE(Ministry of Education in China) Project of Humanities and Social Sciences (No.20YJAZH112), the Hubei Provincial Department of Education Outstanding Youth Scientific Innovation Team Support Foundation (No.T201410, T2020017), the Science and Technology Research Projects of Hubei Provincial Department of Education (No.Q20162706).

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Authors and Affiliations

Authors

Contributions

Zenggang Xiong: Supervision, Conceptualization, Methodology, Writing - Review & Editing. Minyang Zeng: Software, Validation, Formal analysis, Writing - Original Draft, Visualization. Xuemin Zhang: Project administration, Conceptualization. Sanyuan Zhu: Methodology, Formal analysis. Fang Xu: Visualization, Writing - Review & Editing. Xiaochao Zhao: algorithm implementation, Writing - Review & Editing. Yunyun Wu: Simulation ,Writing - Review & Editing. Xiang Li: Simulation, algorithm implementation.

Corresponding author

Correspondence to Zhu Sanyuan.

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For this type of study formal consent was not required. This manuscript does not contain any studies with human participants or animals performed by any of the authors.

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The authors declare that they have no competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Zenggang, X., Mingyang, Z., Xuemin, Z. et al. Social Similarity Routing Algorithm based on Socially Aware Networks in the Big Data Environment. J Sign Process Syst 94, 1253–1267 (2022). https://doi.org/10.1007/s11265-022-01790-3

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  • DOI: https://doi.org/10.1007/s11265-022-01790-3

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