Use of Classification Models for Optimize Link Prediction in the Ego-Social Networks
Subject Areas : Electronics Engineering
Soheila Nemati
1
,
Mehdi sadeghzadeh
2
,
maziyar ganjoo
3
1 - Department of Computer Engineering, Faculty of Engineering, Islamic Azad University, Bushehr Branch, Bushehr, Iran
2 - Department of Computer Engineering, Faculty of Electrical and Computer Engineering, Mahshahr Branch, Islamic Azad University, Mahshahr, Iran
3 - Department of Computer Engineering, Faculty of Engineering, Islamic Azad University, Bushehr Branch, Bushehr, Iran
Keywords:
Abstract :
Social propositional systems are a new generation of systems that use the social network as a user modeling platform to maximize some challenges by using rich interactive data volumes. To make Social networking sites offer new friends to registered users based on local graph features. The main purpose of the link prediction problem on social networks is to suggest a list of users to a particular user that they will probably be communicating in the future. In this research, a prediction method for the link is presented based on the characteristics of classification models. Here, the prediction problem of the link is transformed into a classifying problem with two positive and negative classes, where the positive class represents the relationship and the negative class indicates that the two users are not communicating. Three classical classes DT, NN and NB are used for classification work. To create the dataset, the features of credibility, optimism, number of neighbors, the number of paths of different lengths, the number of shared tweets, the number of internal and external axes are used. Although self-centered grids do not have much overlap in the rings, experiments show that the consideration of self-directed pathways significantly improves predictive performance. The DT classification has recorded the best performance with an average accuracy of 99.85%.
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[27] F.Aghabozorgi, and M.R.Khayyambashi, “A new similarity measure for link prediction based on local structures in social networks,” Physica A: Statistical Mechanics and its Applications, vol.501, pp.12-23. 2018
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_||_[1] D. Liben‐Nowell and J.Kleinberg, “The link‐prediction problem for social networks,” journal of the Association for Information Science and Technology, vol.58, no. 7, pp.1019-1031, 2007.
[2] T. E. Webster, “The Need to Engage With Smartphones and Social Network Sites (SNSs) at Korean Universities,” Recent Developments in Technology-Enhanced and Computer-Assisted Language Learning, pp. 122–143, 2020.
[3] K. Li, L. Tu, and L. Chai, “Ensemble-model-based link prediction of complex networks,” Computer Networks, vol. 166, pp. 106978, 2020.
[4] Y. Sada, J. Hou, P. Richardson, H. El-Serag, and J. Davila, “Validation of Case Finding Algorithms for Hepatocellular Cancer From Administrative Data and Electronic Health Records Using Natural Language Processing,” Medical Care, vol. 54, no. 2, 2016.
[5] S.Samanta, and M. Pal, “Link Prediction in Social Networks,” Graph Theoretic Approaches for Analyzing Large-Scale Social Networks, 164. 2017
[6] S.Rafiee, C.Salavati, and A. Abdollahpouri, “CNDP: Link prediction based on common neighbors degree penalization,” Physica A: Statistical Mechanics and its Applications, vol.539, pp.122950, 2020.
[7] D. Liben-Nowell, and J. Kleinberg, “The Link-prediction Problem for Social Networks,” Journal of the American Society for Information Science and Technology, vol.58, no.7, pp.1019-1031, 2007.
[8] H. Mohammad, “Link Prediction In Social Networks, Indiana university,” Social Network Data Analytics, pp. 243-275, 2011.
[9] M.Jalili, Y.Orouskhani, M.Asgari, N.Alipourfard, and M. Perc, “Link prediction in multiplex online social networks,” Royal Society Open Science, vol.4, no.2, pp.160863, 2017.
[10] W.Cui, C.Pu, Z.Xu, S.Cai, J.Yang, and A.Michaelson, , “Bounded link prediction in very large networks,” Physica A: Statistical Mechanics and its Applications, vol.457, pp.202-214, 2016.
[11] F.Parvazeh, A.Harounabadi, and M. A.Naizari, “A Recommender System for Making Friendship in Social Networks Using Graph Theory and users profile,” Journal of Current Research in Science, no.1, pp.535, 2016.
[12] F. Liu, B.Liu, C.Sun, M.Liu, and X. Wang, “Deep belief network-based approaches for link prediction in signed social networks,” Entropy, vol.17, no.4, pp.2140-2169, 2015.
[13] R.Laishram, K.Mehrotra, and C. K. Mohan, “Link Prediction in Social Networks with Edge Aging,” In proc.In Tools with Artificial Intelligence (ICTAI), November 2016, pp. 606-613.
[14] M.Naderipour,S.Bastani, and M. F.Zarandi, “A Type-2 Fuzzy Model for Link Prediction in Social Network. World Academy of Science, Engineering and Technology,” International Journal of Computer, Electrical, Automation, Control and Information Engineering,” vol.10, no.7, pp.1355-1360, 2016.
[15] P. L. Szczepański, A. S.Barcz, T.P.Michalak, and T. Rahwan, “The game-theoretic interaction index on social networks with applications to link prediction and community detection,” In proc Twenty-Fourth International Joint Conference on Artificial Intelligence , June 2015, pp. 638-644.
[16] J.Zhao, L. Miao, J.Yang, H.Fang, Q. M.Zhang, M.Nie, and T. Zhou, “Prediction of links and weights in networks by reliable routes,” Scientific reports, vol.5, pp.12261, 2015.
[17] S.Han, and Y. Xu, “Link Prediction in Microblog Network Using Supervised Learning with Multiple Features, ” in JCP, vol.11, no.1, pp.72-82, 2016.
[18] M.Zhang, , Z.Cui, , S.Jiang, and Y.Chen, “Beyond Link Prediction: Predicting Hyperlinks in Adjacency Space, ” AAAI-2018 , pp. 4430-4437, 2018.
[19] Z.Huo, X.Huang, and X. Hu, “Link Prediction with Personalized Social Influence,” in proc.Conference on Artificial Intelligence, 2018, pp. 136-139.
[20] P.Pei, B.Liu, and L. Jiao, “Link prediction in complex networks based on an information allocation index,” Physica A: Statistical Mechanics and its Applications, vol.470, pp.1-11, 2017.
[21] J.Leskovec, and J.Mcauley, “Learning to discover social circles in ego networks,” In Advances in neural information processing systems, pp. 539-547, 2012.
[22] K. K.Shang, , M.Small, , X. K.Xu, and W. S. Yan, “The role of direct links for link prediction in evolving networks,” EPL (Europhysics Letters), vol.117, no.2, pp.28002, 2017.
[23] S.Sharma, and A.Singh, “An efficient method for link prediction in weighted multiplex networks,” Computational Social Networks, vol.3, no. 1, pp.7, 2016.
[24] A.Anagnostopoulos, J.Łącki, S. Lattanzi, S. Leonardi, and M. Mahdian, “Community detection on evolving graphs,” In Advances in Neural Information Processing Systems, pp. 3522-3530, 2016.
[25] A.Papadimitriou, P.Symeonidis, and Y.Manolopoulos, “Fast and accurate link prediction in social networking systems,” Journal of Systems and Software, vol.85, no. 9, pp. 2119-2132, 2012
[26] L.Bai, X.Cheng, J. Liang, and Y.Guo, “Fast graph clustering with a new description model for community detection,” Information Sciences, vol. 388- 389, pp. 37-47, 2017.
[27] F.Aghabozorgi, and M.R.Khayyambashi, “A new similarity measure for link prediction based on local structures in social networks,” Physica A: Statistical Mechanics and its Applications, vol.501, pp.12-23. 2018
[28] X.Cao, Y.Zheng, C.Shi, J.Li, & B.Wu, “Meta-path-based link prediction in schema-rich heterogeneous information network,” International Journal of Data Science and Analytics, vol.3, no.4, pp.285-296, 2017.