Skip to main content
SpringerLink
Log in

Construction of online social network data mining model based on blockchain

  • Focus
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

In order to overcome the problems of low security and accuracy of traditional online social network data mining model, this paper proposes and constructs a new online social network data mining model based on blockchain. Based on the blockchain structure, the hash function in the blockchain is used to encrypt the online social network data to improve the security of the data. Based on the encrypted data, the fuzzy covariance matrix is used to obtain the data clustering center to complete the data clustering. According to the data clustering results, time series analysis method is used to mine online social network data. Experimental results show that, compared with the traditional mining model, this model can effectively improve the security of online social network data and improve the mining accuracy, with the highest mining accuracy of 97%.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig.1
Fig. 2
Fig. 3

Similar content being viewed by others

Data Availability

All data generated or analyzed during this study are included in the manuscript.

References

  • Chen F, Yuanbo G, Na W et al (2020) Differential private data publishing method based on generative adversarial network[J]. Acta Electron Sin 48(10):113–122

    Article  Google Scholar 

  • Chuhan AI, Di Jiang Wu, Jiande, (2019) Research on patent text data mining based on topic model and association rules[J]. J North Univ China (Natural Science Edition) 40(6):524–530

    Google Scholar 

  • Dai Mingzhu, Gao Songfeng. Research on Data Mining Algorithm Based on Examination Process and Knowledge Structure[J]. Computer Science, 2018, 045(0z1):437–441

  • Gao J, Wang H, Shen H (2020) Task failure prediction in cloud data centers using deep learning. IEEE Trans Serv Comput. https://doi.org/10.1109/tsc.2020.2993728

    Article  Google Scholar 

  • Gengxin S, Sheng B (2018) Network public opinion propagation model based on interest matching in multiple relationship social network[J]. J Univ Sci Technol China 048(009):730–738

    MATH  Google Scholar 

  • Genxia Y (2019) Big data mining algorithm based on association rules[J]. Microelectron Comput 36(04):111–114

    Google Scholar 

  • Li C, Hongxiao F, Hailun D et al (2019) A data sampling method based on double decision tree[J]. Computer Eng Sci 41(01):134–139

    Google Scholar 

  • Liang Jie, Chen Jiahao, Zhang Xueqin, et al. (2019) One-hot encoding and convolutional neural network based anomaly detection[J]. J Tsinghua Univ(Science and Technology), 059(007):523–529

  • Linbing Z, Guo Qiang Wu, Xingbin, et al (2020) User clustering method based on multi-dimensional behavior analysis[J]. J Univ Electron Sci Technol China 049(002):315–320

    Google Scholar 

  • Mochón F, Sanjuan-Martinez O (2014) A first approach to the implicit measurement of happiness in Latin America through the use of social networks. Int J Interact Multimedia Artificial Intell 2(5):16. https://doi.org/10.9781/ijimai.2014.252

    Article  Google Scholar 

  • Panpan F, Jifu Z (2018) Relative subspaces and outlier mining in gaussian mixture model[J]. J Chinese Comput Syst 39(11):141–146

    Google Scholar 

  • Pham DV, Nguyen GL, Nguyen TN, Pham CV, Nguyen AV (2020) Multi-topic misinformation blocking with budget constraint on online social networks. IEEE Access 8:78879–78889. https://doi.org/10.1109/ACCESS.2020.2989140

    Article  Google Scholar 

  • Raj ED, Babu LD (2016) A fuzzy adaptive resonance theory inspired overlapping community detection method for online social networks. Knowl-Based Syst 113:75–87. https://doi.org/10.1016/j.knosys.2016.09.015

    Article  Google Scholar 

  • Raj ED, Babu LD (2017) An enhanced trust prediction strategy for online social networks using probabilistic reputation features. Neurocomputing 219:412–421. https://doi.org/10.1016/j.neucom.2016.09.036

    Article  Google Scholar 

  • Raj ED, Manogaran G, Srivastava G, Wu Y (2021) Information granulation-based community detection for social networks. IEEE Trans Comput Soc Syst 8(1):122–133. https://doi.org/10.1109/tcss.2019.2963247

    Article  Google Scholar 

  • Wang S et al (2016) Collaboration reputation for trustworthy web service selection in social networks. J Comput Syst Sci 82(1):130–143. https://doi.org/10.1016/j.jcss.2015.06.009

    Article  MathSciNet  Google Scholar 

  • Xiao Wen Hu, Juan ZX (2018) Parallel association rules mining algorithm based on MapReduce:a survey[J]. Appl Res Comput 35(1):13–23

    Google Scholar 

  • Xiaohong Li, Zhenyun C (2020) Improved fuzzy clustering algorithm for data mining model in big data[J]. Modern Electronics Technique 043(003):177–182

    Google Scholar 

  • Xuesong Z, Caiyan J (2018) A new documents clustering method based on frequent itemsets[J]. J Comput Res Develop 55(1):102–112

    Google Scholar 

  • Ye Xiaoying, Wan Mei, Tang Rong, et al. (2020) Clustering algorithm of social network based on graph clustering and ant colony optimization algorithm[J]. Appl Res Computers, 37(06):76–80+93

  • Yifan J, Qing Ye (2018) Time series similarity measure based on Siamese neural network[J]. J Comput Appl 39(4):1041–1045

    Google Scholar 

  • Ying Y, Lixin Ji, Xiaotao C et al (2019) Heterogeneous network representation learning based on homogeneous subgraghs transformation[J]. Comput Eng 045(011):204–212

    Google Scholar 

  • Yonggong R, Liu Y, Yang L (2019) Heat diffusion influence propagation based personalized recommendation algorithm for social network[J]. Pattern Recognit Artific Intell 32(8):746–757

    Google Scholar 

  • Yongmei W, Yongai Z, Yingying W (2019) Research and implementation of ship operation data mining algorithm based on collaborative filtering[J]. Ship Sci Technol 41(08):98–100

    Google Scholar 

  • Yuan Deyu, Zhang Yifan, Gao Jian, et al. (2020) Abnormal User Detection Method in Sina Weibo Based on User Feature Extraction [J]. Comput Sci, 47(S1):364–368+385

  • Zhang Bo, Li Ge (2019) Design of Web anomaly data mining software based on improved clustering algorithm[J]. Modern Electronics Technique, 42(08):81–84+89

  • Zhang Yongchao (2019) Hybrid Mining Model Simulation Based on Deep Belief Network Database[J]. Computer Simulat, 036(012):313–316,360

Download references

Funding

No funds, grants were received by any of the authors.

Author information

Authors and Affiliations

  1. School of Computer, Weinan Normal University, Weinan, Shaanxi, China

    Xiaoqiang Jia

Authors
  1. Xiaoqiang Jia
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Xiaoqiang Jia contributed to the design and methodology of this study, the assessment of the outcomes and the writing of the manuscript.

Corresponding author

Correspondence to Xiaoqiang Jia.

Ethics declarations

Conflict of interest

There is no conflict of interest among the authors.

Additional information

Communicated by Oscar Sanjuán Martínez.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jia, X. Construction of online social network data mining model based on blockchain. Soft Comput 27, 5137–5145 (2023). https://doi.org/10.1007/s00500-021-06131-3

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-021-06131-3

Keywords

Search

Navigation