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Friendship Prediction in Social Networks Using Developed Extreme Learning Machine with Kernel Reduction and Probabilistic Calculation

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Part of the Lecture Notes in Networks and Systems book series (LNNS,volume 457)

Abstract

The social network remains a highly dynamic object. Friendship prediction presents a significant problem in the research in network application in general and in social networking applications in particular. It involves analyzing an existing network graph and predicting more links inside the graph that were not identified before. Various models and approaches were developed for this purpose. Similarity-based models were used extensively, mainly they suffered from non-capability of handling the changing nature of the graph. Other models have supervised models that require training on labelled data. However, they need the extraction of many features to achieve satisfying performance. This work provides a novel implicit link prediction probabilistic reduced kernel extreme learning machine named ILP-PRKELM. Unlike the traditional supervised model of link prediction, ILP-PRKELM is attributed to the capability of achieving absolute accuracy with less number of features. Experimental results showed the superiority of ILP-PRKELM with an accomplished accuracy of 84.6 and 78.6 for Last.fm and Douban respectively, which is equivalent to 2% improved accuracy over the benchmarks.

Keywords

  • Link prediction
  • Extreme learning machine
  • Social network
  • Friend relationship
  • Implicit relationship
  • Explicit relationship

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References

  1. Zhou, Z., Chen, X., Li, E., Zeng, L., Luo, K., Zhang, J.: Edge intelligence: paving the last mile of artificial intelligence with edge computing. Proc. IEEE 107(8), 1738–1762 (2019)

    CrossRef  Google Scholar 

  2. Katzman, J.L., Shaham, U., Cloninger, A., Bates, J., Jiang, T., Kluger, Y.: DeepSurv: personalized treatment recommender system using a cox proportional hazards deep neural network. BMC Med. Res. Methodol. 18(1), 1–12 (2018)

    CrossRef  Google Scholar 

  3. Abd Alkhalec Tharwat, M.E., Jacob, D.W., Md Fudzee, M.F., Kasim, S., Ramli, A.A., Lubis, M.: The role of trust to enhance the recommendation system based on social network. Int. J. Adv. Sci. Eng. Inf. Technol. 10(4), 1387–1395 (2020)

    CrossRef  Google Scholar 

  4. Kirichenko, L., Radivilova, T., Anders, C.: Detecting cyber threats through social network analysis: short survey. Socioecon. Challenges 1, 20–34 (2017)

    CrossRef  Google Scholar 

  5. Sandefur, C.I., Mincheva, M., Schnell, S.: Network representations and methods for the analysis of chemical and biochemical pathways. Mol. Biosyst. 9(9), 2189–2200 (2013)

    CrossRef  Google Scholar 

  6. Blazewicz, J., et al.: Graph algorithms for DNA sequencing – origins, current models and the future. Eur. J. Oper. Res. 264(3), 799–812 (2018)

    MathSciNet  CrossRef  Google Scholar 

  7. Nia, R., Erlandsson, F., Johnson, H., Wu, S.F.: Leveraging social interactions to suggest friends. In: Proceedings - International Conference on Distributed Computing Systems, pp. 386–391 (2013)

    Google Scholar 

  8. Lin, J., Ban, Y.: Comparative analysis on topological structures of urban street networks. ISPRS Int. J. Geo-Inf. 6(10), 295 (2017)

    CrossRef  Google Scholar 

  9. Li, N., Member, S., Díaz, V.H., Antonio, J., Fernandez, S.: Probability prediction-based reliable and efficient opportunistic routing algorithm for VANETs. IEEE/ACM Trans. Netw. 26(4), 1933–1947 (2018)

    CrossRef  Google Scholar 

  10. Pandey, B., Bhanodia, P.K., Khamparia, A., Pandey, D.K.: A comprehensive survey of edge prediction in social networks: techniques, parameters and challenges. Expert Syst. Appl. 124, 164–181 (2019)

    CrossRef  Google Scholar 

  11. Tharwat, M.E.A.A., Fudzee, M.F.M., Kasim, S., Ramli, A.A., Ali, M.K.: Multi-objective NSGA-II based community detection using dynamical evolution social network. Int. J. Electr. Comput. Eng. 11(5), 4502–4512 (2021)

    Google Scholar 

  12. Yin, L., Zheng, H., Bian, T., Deng, Y.: An evidential link prediction method and link predictability based on Shannon entropy. Phys. A Stat. Mech. Appl. 482, 699–712 (2017)

    MathSciNet  CrossRef  Google Scholar 

  13. Yu, W., Lin, X., Zhang, W., Pei, J., McCann, J.A.: SimRank*: effective and scalable pairwise similarity search based on graph topology. VLDB J. 28(3), 401–426 (2019). https://doi.org/10.1007/s00778-018-0536-3

    CrossRef  Google Scholar 

  14. Peng, W., Baowen, X.U., Yurong, W.U., Xiaoyu, Z.: Link prediction in social networks: the state-of-the-art. Sci. China Inf. Sci. 58(January), 1–38 (2015)

    Google Scholar 

  15. Leskovec, J., Huttenlocher, D., Kleinberg, J.: Predicting positive and negative links in online social networks. In: Proceedings of the 19th International Conference on World Wide Web, pp. 641–650 (2010)

    Google Scholar 

  16. Munasinghe, L., Ichise, R.: Link prediction in social networks using information flow via active links. IEICE Trans. Inf. Syst. 96(E96-D), 1495–1502 (2013)

    CrossRef  Google Scholar 

  17. Kumar, A., Singh, S.S., Singh, K., Biswas, B.: Level-2 node clustering coefficient-based link prediction. Appl. Intell. 49(7), 2762–2779 (2019). https://doi.org/10.1007/s10489-019-01413-8

    CrossRef  Google Scholar 

  18. Bütün, E., Kaya, M.: A pattern based supervised link prediction in directed complex networks. Phys. A Stat. Mech. its Appl. 525, 1136–1145 (2019)

    CrossRef  Google Scholar 

  19. Chao Li, J., Ling Zhao, D., Ge, B.F., Yang, K.W., Chen, Y.W.: A link prediction method for heterogeneous networks based on BP neural network. Phys. A Stat. Mech. Appl. 495, 1–17 (2018)

    CrossRef  Google Scholar 

  20. Nguyen-Thi, A.T., Nguyen, P.Q., Ngo, T.D., Nguyen-Hoang, T.A.: Transfer AdaBoost SVM for link prediction in newly signed social networks using explicit and PNR features. Procedia Comput. Sci. 60(1), 332–341 (2015)

    CrossRef  Google Scholar 

  21. Zhang, M., Chen, Y.: Link prediction based on graph neural networks. In: Advances in Neural Information Processing Systems, NeurIPS, pp. 5165–5175 (2018)

    Google Scholar 

  22. Suryakant, Mahara, T.: A new similarity measure based on mean measure of divergence for collaborative filtering in sparse environment. Procedia Comput. Sci. 89, 450–456 (2016)

    Google Scholar 

  23. Cantador, I., Brusilovsky, P., Kuflik, T.: Second workshop on information heterogeneity and fusion in recommender systems (HetRec2011). In: Proceedings of the Fifth ACM Conference on Recommender Systems, HetRec, pp. 387–388 (2011)

    Google Scholar 

  24. Lyu, M.R., Ave, P., Park, F.: Recommender systems with social regularization. In: WSDM 2011 Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, pp. 287–296 (2011)

    Google Scholar 

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Acknowledgments

This work is supported by Ministry of Higher Education (MOHE) under Fundamental Research Grant Scheme (FRGS) reference code FRGS/1/2018/ICT04/UTHM/02/3.

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Correspondence to Muhammed E. Abd Alkhalec Tharwat .

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Tharwat, M.E.A.A., Fudzee, M.F.M., Kasim, S., Ramli, A.A., Madni, S.H.H. (2022). Friendship Prediction in Social Networks Using Developed Extreme Learning Machine with Kernel Reduction and Probabilistic Calculation. In: Ghazali, R., Mohd Nawi, N., Deris, M.M., Abawajy, J.H., Arbaiy, N. (eds) Recent Advances in Soft Computing and Data Mining. SCDM 2022. Lecture Notes in Networks and Systems, vol 457. Springer, Cham. https://doi.org/10.1007/978-3-031-00828-3_6

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