Trust Based Link Prediction Using Fuzzy Computational Model in Social Networks
Subject Areas : AI and Robotics
Fateme Hoseinkhani
1
,
Ali Harounabadi
2
,
ُُُُSaeed Setayeshi
3
1 - Department of Computer Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran
2 - Department of Computer Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran
3 - Nuclear Engineering and Physics Dept., Amirkabir University of Technology, Tehran, Iran
Keywords: Link prediction, Signed social networks, Trust, Distrust, Fuzzy computational,
Abstract :
Link prediction is an important to check link between nodes in social networks. The modeling of social networks leads to emergence of signed, directed and weighted social networks. The relationships of users in social networks are characterized by subjective, asymmetric and ambiguous aspects related to this domain, then both terms of trust and distrust are challenging. To solve the problem of sparsity in networks and overcome ambiguity in relationships, a trust-distrust method based on fuzzy computational is proposed to calculate strength of links. The purpose of proposed link prediction is to solve problem of sparsity in signed social networks by combining descriptive features of users with the direct influence of top nodes and the indirect influence of common nodes on rating prediction. Trust is determined by a Mamdani fuzzy system based on mirroring of similarity fuzzy features, overall trust and overall distrust. The evaluation of the proposed method was done with the accuracy measure on datasets of Epinions and Slashdot. The accuracy of proposed method in Epinions and Slashdot datasets is 0.991 and 0.998, respectively. The obtained results show that proposed method works well for problem of data sparsity in signed social networks and show the effectiveness of proposed model.
[1] X.Li, H.Fang, and J.Zhang, “FILE: A novel framework for predicting social status in signed networks”, Thirty-Second AAAI Conference on Artificial Intelligence, AAAI18 - Artificial Intelligence and the Web, Vol.32, No.1, 2018, PP.330–337.
[2] K.Akilal, M.Omar, and H.Slimani, “Characterizing and using gullibility, competence, and reciprocity in a very fast and robust trust and distrust inference algorithm for weighted signed social networks”, Knowledge-Based Systems, Vol.192, 2020, PP.1-11.
[3] H.Shirgahi, M.Mohsenzadeh, and H.H.S.Javadi, “A new method of trust mirroring estimation based on social networks parameters by fuzzy system”, International Journal Machine Learning & Cybernetics, Springer. Vol.9, 2018, PP.1153–1168.
[4] V.Kant, and KK.Bharadwaj, Fuzzy computational models of trust and distrust for enhanced recommendations, International Journal of Intelligent Systems, Vol.28, 2013, PP.332–365.
[5] N.Girdhar, S.Minz, and K.K.Bharadwaj, “Link prediction in signed social networks based on fuzzy computational model of trust and distrust”, Soft Computing, Vol.23, 2019, PP.12123–12138.
[6] Z.Duan, W.Xu, Y.Chen, and L.Ding, “ETBRec: a novel recommendation algorithm combining the double influence of trust relationship and expert users”, Applied Intelligence, Vol.52, 2022, PP.282–294.
[7] N.D.Nur, A.H.Sitil, S.Muntadher, S.Firdaus, and N.Anuar, “Applications of link prediction in social networks: A review”, Journal of Network and Computer Appllicatins, Vol.166, 2020, PP.1-31.
[8] X.Zhu, and Y.Ma, “Sign Prediction on Social Networks Based Nodal Features”, Journal of Complexity, Vol.2020, 2020, PP.1-11.
[9] R.E.Veras De Sena Rosa, F.A.S.Guimarães, R.d.S.Mendonça and V.F.d.Lucena, “Improving Prediction Accuracy in Neighborhood-Based Collaborative Filtering by Using Local Similarity”, IEEE Access, Vol.8, 2020, PP.142795-142809.
[10] H.Ghorbanzadeh, A.Sheikhahmadi, M.Jalili, and S.Sulaimany, “A Hybrid Method of Link Prediction in Directed Graphs”, Expert Systems with Applications, Vol.165, 2020, PP.1-13.
[11] D.Wang, Da-wei, Y.Yih and M.Ventresca, “Improving neighbor-based collaborative filtering by using a hybrid similarity measurement”, Expert Systems with Applications, Vol.160, 2020, PP.1-23.
[12] X.Wang, Y.Chai, H.Li, and D.Wu, “Link prediction in heterogeneous information networks: An improved deep graph convolution approach”, Decision Support Systems, Vol.141, 2021, PP.113448-113460.
[13] H.Tahmasbi, M.Jalali, and H.Shakeri, “TSCMF: Temporal and social collective matrix factorization model for recommender systems”, Journal of Intelligence Information Systems, Vol.56, 2021, PP.169–187.
[14] C.He, H.Liu, Y.Tang, S.Liu, X.Fei, Q.Cheng, and H.Li, “Similarity preserving overlapping community detection in signed networks”, Future Generation Computer Systems, Vol.116, 2021, PP.275-290.
[15] R.I.Yaghi, H.Faris, I.Aljarah, A.M.Al-Zoubi, A.A.Heidari, and S.Mirjalili, “Link Prediction Using Evolutionary Neural Network Models”, Evolutionary Machine Learning Techniques, Vol.32, 2020, PP.85-112.
[16] R.E.Tillman, P.Vamsi, Ch.Jiahao, R.Prashant and M.Veloso, “Heuristics for Link Prediction in Multiplex Networks”, In Proceedings of ECAI'2020, 24th European Conference on Artificial Intelligence, Vol.325, 2020, PP.1938-1945.
[17] F.Guo, W.Zhou, Z.Wang, Ch.Ju, Sh.Ji, Q.Lu, "A link prediction method based on topological nearest-neighbors similarity in directed networks", Journal of Computational Science, Vol.69, 2023, PP.102002-102016.
[18] H.Liu, Z.Zhenzhen, B.Fan, H.Zeng, Y.Zhang, and G.Jiang, “PrGCN: Probability prediction with graph convolutional network for person re-identification”, Neurocomputing, Vol.423, 2021, PP.57-70.
[19] X.Hu, X.Xiong, Y.Wu, M.Shi, P.Wei, and Ch.Ma, "A Hybrid Clustered SFLA-PSO algorithm for optimizing the timely and real-time rumor refutations in Online Social Networks", Expert Systems with Applications, Vol.212, 2023, PP.118638-118670.
[20] Y.Xu, Z.Feng, X.Zhou, M.Xing, H.Wu, X.Xue, Sh.Chen, Ch.Wang and L.Qi, "Attention-based neural networks for trust evaluation in online social networks", Information Sciences, Vol.630, 2023, PP.507-522.
[21] M.Nooraei.Abadeh, M.Mirzaie, “A differential machine learning approach for trust prediction in signed social networks”, Supercomput, Vol.79, 2023, PP.9443–9466.
[22] T.Zhang, W.Li, L.Wang, and J.Yang, “Social recommendation algorithm based on stochastic gradient matrix decomposition in social network”, Journal of Ambient Intelligence and Humanized Computing. Vol.11, 2020, PP.601-608.
[23] P.Srilatha, R.Manjula, and C.P.Kumar, “Link Prediction on Social Attribute Network Using Lévy Flight Firefly Optimization”, Advances in Artificial Intelligence and Data Engineering, Vol.1133, 2021, PP.1299-1309.
[24] E.Nasiri, K.Berahmand, and Y.Li, “Robust graph regularization nonnegative matrix factorization for link prediction in attributed networks”, Multimedia Tools and Applications, Vol.82, 2023, PP.3745–3768.
[25] S.Ghasemi, and A.Zarei., “Improving link prediction in social networks using local and global features: a clustering-based approach, Progress in Artificial”, Intelligence, Vol.11, 2022, PP.79–92.
[26] Suryakant, and T.Mahara, A New Similarity Measure Based on Mean Measure of Divergence for Collaborative Filtering in Sparse Environment, Procedia Computer Science, Vol.89, 2016, PP.450–456.
[27] https://snap.stanford.edu/data/soc-Epinions1. html,Last Visited (01, October. 2022).
[28] http://snap.stanford.edu/data/soc-Slashdot0902.html, Last Visited (01, October.2022).
[29] J.Golbeck, “Combining provenance with trust in social networks for semantic content filtering”, International Provenance and Annotation Workshop, Vol.4145, 2006, PP.101–108.
