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Follower Link Prediction Using the XGBoost Classification Model with Multiple Graph Features

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Abstract

The Follower Link Prediction is an emerging application preferred by social networking sites to increase their user network. It helps in finding potential unseen individual and can be used for identifying relationship between nodes in social network. With the rapid growth of many users in social media, which users to follow leads to information overload problems. Previous works on link prediction problem are generally based on local and global features of a graph and limited to a smaller dataset. The number of users in social media is increasing in an extraordinary rate. Generating features for supervised learning from a large user network is challenging. In this paper, a supervised learning model (LPXGB) using XGBoost is proposed to consider the link prediction problem as a binary classification problem. Many hybrid graph feature techniques are used to represent the dataset suitable for machine learning. The efficiency of the LPXGB model is tested with three real world datasets Karate, Polblogs and Facebook. The proposed model is compared with various machine learning classifiers and also with traditional link prediction models. Experimental results are evident that the proposed model achieves higher classification accuracy and AUC value.

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References

  1. Wu, K. S., & Chang, P. C. (2016). Identifying communities and influential node in Facebook fan page - A case study of FJU 2013 ad camp. International Journal of Web Based Communities, 12, 376–392. https://doi.org/10.1504/IJWBC.2016.080813.

    Article  Google Scholar 

  2. Papadimitriou, A., Symeonidis, P., & Manolopoulos, Y. (2012). Fast and accurate link prediction in social networking systems. Journal of Systems and Software, 85, 2119–2132. https://doi.org/10.1016/j.jss.2012.04.019.

    Article  Google Scholar 

  3. Watson, B., Watson, T., & Zheng, J. (2019). A study of friend recommendations for gaming communities. International Journal of Web Based Communities, 15, 292–314.

    Article  Google Scholar 

  4. Anandhan, A., Shuib, L., Ismail, M. A., & Mujtaba, G. (2018). Social media recommender systems: Review and open research issues. IEEE Access., 6, 15608–15628. https://doi.org/10.1109/ACCESS.2018.2810062.

    Article  Google Scholar 

  5. Li, Y., Luo, P., Fan, Z., & ping, Chen, K., Liu, J. . (2017). A utility-based link prediction method in social networks. European Journal of Operational Research, 260, 693–705. https://doi.org/10.1016/j.ejor.2016.12.041.

    Article  MathSciNet  MATH  Google Scholar 

  6. Papadimitriou, A., Symeonidis, P., & Manolopoulos, Y. (2012). Scalable link prediction in social networks based on local graph characteristics. In Proceedings of 9th International Conference on Information Technology ITNG 2012 (pp. 738–743).https://doi.org/10.1109/ITNG.2012.145

  7. Marjan, M., Zaki, N., & Mohamed, E. A. (2018). Link prediction in dynamic social networks: A literature review. Colloquium in Information Science and Technology. https://doi.org/10.1109/CIST.2018.8596511.

    Article  Google Scholar 

  8. Kumar, A., Singh, S. S., Singh, K., & Biswas, B. (2020). Link prediction techniques, applications, and performance: A survey. Physica A Statistical Mechanics and Its Applications. https://doi.org/10.1016/j.physa.2020.124289.

    Article  MathSciNet  MATH  Google Scholar 

  9. Han, S., & Xu, Y. (2016). Link prediction in microblog network using supervised learning with multiple features. Journal of Computation, 11, 72–82. https://doi.org/10.17706/jcp.11.1.72-82.

    Article  Google Scholar 

  10. Cukierski, W., Hamner, B., & Yang, B. (2011). Graph-based features for supervised link prediction. Proceedings of the International Joint Conference on Neural Networks. https://doi.org/10.1109/IJCNN.2011.6033365.

    Article  Google Scholar 

  11. Liben-Nowell, D., & Kleinberg, J. (2007). The link-prediction problem for social networks. Journal of the American Society for Information Science and Technology, 58, 1019–1031. https://doi.org/10.1002/asi.20591.

    Article  Google Scholar 

  12. Gupta, A. K., & Sardana, N. (2018). Prediction of missing links in social networks: Feature integration with node neighbour. International Journal of Web Based Communities, 14, 38–53. https://doi.org/10.1504/IJWBC.2018.090917.

    Article  Google Scholar 

  13. Dong, L., Li, Y., Yin, H., Le, H., & Rui, M. (2013). The algorithm of link prediction on social network. . Math: Probl. Eng. https://doi.org/10.1155/2013/125123.

    Book  Google Scholar 

  14. Rahman, M.S., Dey, L.R., Haider, S., Uddin, M.A., & Islam, M. (2017). Link prediction by correlation on social network. In 20th International Conference of Computer and Information Technology, ICCIT 2017 (pp. 1–6). https://doi.org/10.1109/ICCITECHN.2017.8281812.

  15. Yadav, R. K., Tripathi, S. P., Rai, A. K., & Tewari, R. R. (2020). Hybrid feature-based approach for recommending friends in social networking systems. International Journal of Web Based Communities, 16, 51–71.

    Article  Google Scholar 

  16. Dong, Y., Tang, J., Wu, S., Tian, J., Chawla, N. V., Rao, J., & Cao, H. (2012). Link prediction and recommendation across heterogeneous social networks. In Proceeding of IEEE international conference on data mining, ICDM (pp. 181–190). https://doi.org/10.1109/ICDM.2012.140.

  17. Lin, S., Liu, C., & Zhang, Z. K. (2017) Multi-tasking link prediction on coupled networks via the factor graph model. In Proceedings of IECON 2017 - 43rd Annual Conference on IEEE Industry Electronics Society (pp. 5570–5574). https://doi.org/10.1109/IECON.2017.8216964.

  18. Najari, S., Salehi, M., Ranjbar, V., & Jalili, M. (2019). Link prediction in multiplex networks based on interlayer similarity. Physica A: Statistical Mechanics and its Applications, 536, 120978. https://doi.org/10.1016/j.physa.2019.04.214.

    Article  MATH  Google Scholar 

  19. Sharma, P. K., Rathore, S., & Park, J. H. (2019). Multilevel learning based modeling for link prediction and users’ consumption preference in Online Social Networks. Future Generation Computer Systems, 93, 952–961. https://doi.org/10.1016/j.future.2017.08.031.

    Article  Google Scholar 

  20. Wu, J., Shen, J., Zhou, B., Zhang, X., & Huang, B. (2019). General link prediction with influential node identification. Physica A: Statistical Mechanics and its Applications, 523, 996–1007. https://doi.org/10.1016/j.physa.2019.04.205.

    Article  Google Scholar 

  21. Hao, Z. (2019). Link prediction in online social networks based on the unsupervised marginalized denoising model. IEEE Access., 7, 54133–54143. https://doi.org/10.1109/ACCESS.2019.2912662.

    Article  Google Scholar 

  22. Hisano, R. (2018). Semi-supervised graph embedding approach to dynamic link prediction. . Springer Proc: Complex. https://doi.org/10.1007/978-3-319-73198-8_10.

    Book  Google Scholar 

  23. Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. In International Conference on Knowledge Discovery Data Mining (pp. 785–794).

  24. Behera, D. K., Das, M., Swetanisha, S., Naik, B. (2018). Collaborative filtering using restricted boltzmann machine and fuzzy C-means. In Advances in intelligent systems and computing (pp. 723–731). Springer. https://doi.org/10.1007/978-981-10-7871-2_69.

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Authors and Affiliations

  1. School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar, India

    Dayal Kumar Behera & Madhabananda Das

  2. Department of CSE, Trident Academy of Technology, Bhubaneswar, India

    Subhra Swetanisha

  3. Department of Computer Science and Engineering, Aditya Institute of Technology and Management (AITAM), Tekkali, Andhra Pradesh,, 532201, India

    Janmenjoy Nayak

  4. Department of Information Technology, National Engineering College, Kovilpatti, Tamilnadu, 628503, India

    S. Vimal

  5. Department of Computer Application, Veer SurendraSai University of Technology, Burla, Sambalpur, Odisha, 768018, India

    Bighnaraj Naik

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  1. Dayal Kumar Behera
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  2. Madhabananda Das
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  4. Janmenjoy Nayak
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  5. S. Vimal
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Correspondence to Dayal Kumar Behera.

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Behera, D.K., Das, M., Swetanisha, S. et al. Follower Link Prediction Using the XGBoost Classification Model with Multiple Graph Features. Wireless Pers Commun 127, 695–714 (2022). https://doi.org/10.1007/s11277-021-08399-y

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