پیش‌بینی پیوند در شبکه‌های علمی با استفاده از یادگیری ماشین و گراف‌های وزن‌دار

[1] R. Taimourei-Yansary, M. Mirzarezaee, M. Sadeghi, and B.N. Araabi, “Predicting invasive disease-free survival time in breast cancer patients using semi-supervised graph-based machine learning techniques,” Soft Comput. J., vol. 10, no. 1, pp. 48-69, 2021, doi: 10.22052/scj.2022.243330.1039 [In Persian].
[2] E. Mahfooz and G. Fath-Tabar, “Sum of distance between vertices of graphs,” Soft Comput. J., vol. 5, no. 2, pp. 28-33, 2016, dor: 20.1001.1.23223707.1395.5.2.3.0 [In Persian]. 
[3] A. Keypour, “Link prediction in social networks through classifiers combination,” Soft Comput. J., vol. 4, no. 2, pp. 2-17, 2016, dor: 20.1001.1.23223707.1394.4.2.54.4 [In Persian].
[4] V. Martinez, F. Berzal, and J.-C. Cubero, “A survey of link prediction in complex networks,” ACM Comput. Surv., vol. 49, no. 4, pp. 1-33, 2016, doi: 10.1145/3012704.
[5] L. Lu and T. Zhou, “Link prediction in complex networks: A survey,” Phys. A: Stat. Mech. Appl., vol. 390, no. 6, pp. 1150-1170, 2011, doi: 10.1016/j.physa.2010.11.027.
[6] C.P. Muniz, R. Goldschmidt, and R. Choren, “Combining contextual, temporal and topological information for unsupervised link prediction in social networks,” Knowl.-Based Syst., vol. 156, pp. 129-137, 2018, doi: 10.1016/j.knosys.2018.05.027.
[7] M. Nikkar, R. Alijani, and K.M.H. Ghazizadeh, “Investigation of the presence of surgery researchers in research gate scientific network: An altmetrics study,” Iran. J. Surg., vol. 25, no. 2, pp. 76-82, 2017.
[8] H. Liu, H. Kou, C. Yan, and L. Qi, “Link prediction in paper citation network to construct paper correlation graph,” EURASIP J. Wireless Commun. Netw., vol. 2019, no. 1, pp. 1-12, 2019.
[9] E. Butun and M. Kaya, “Predicting citation count of scientists as a link prediction problem,” IEEE Trans. Cybern., vol. 50, no. 10, pp. 4518-4529, 2019, doi: 10.1109/TCYB.2019.2900495.
[10] N. Shibata, Y. Kajikawa, and I. Sakata, “Link prediction in citation networks,” J. Am. Soc. Inf. Sci. Technol., vol. 63, no. 1, pp. 78-85, 2012, doi: 10.1002/asi.21664.
[11] V. Latora, V. Nicosia, and G. Russo, Complex Networks: Principles, Methods and Applications. Cambridge, U.K.: Cambridge Univ. Press, 2017.
[12] P.M. Chuan, M. Ali, T.D. Khang, and N. Dey, “Link prediction in co-authorship networks based on hybrid content similarity metric,” Appl. Intell., vol. 48, no. 8, pp. 2470-2486, 2018.
[13] E. Butun, M. Kaya, and R. Alhajj, “Extension of neighbor-based link prediction methods for directed, weighted and temporal social networks,” Inf. Sci., vol. 463, pp. 152-165, 2018, doi: 10.1016/j.ins.2018.06.051.
[14] S. Behrouzi, Z. S. Sarmoor, K. Hajsadeghi, and K. Kavousi, “Predicting scientific research trends based on link prediction in keyword networks,” J. Informetrics, vol. 14, no. 4, Art. no. 101079, 2020, doi: 10.1016/j.joi.2020.101079.
[15] A. Daud et al., “Who will cite you back? Reciprocal link prediction in citation networks,” Lib. Hi Tech, vol. 35, no. 4, pp. 509-520, 2017, doi: 10.1108/LHT-02-2017-0044.
[16] D. Liben-Nowell and J. Kleinberg, “The link-prediction problem for social networks,” J. Am. Soc. Inf. Sci. Technol., vol. 58, no. 7, pp. 1019-1031, 2007.
[17] S. Martincic-Ipsic, E. Mocibob, and M. Perc, “Link prediction on Twitter,” PLoS ONE, vol. 12, no. 7, p. e0181079, 2017, doi: 10.1371/journal.pone.0181079.
[18] L.A. Adamic and E. Adar, “Friends and neighbors on the web,” Soc. Netw., vol. 25, no. 3, pp. 211-230, 2003, doi: 10.1016/S0378-8733(03)00009-1.
[19] T. Zhou, L. Lu, and Y.-C. Zhang, “Predicting missing links via local information,” Eur. Phys. J. B, vol. 71, no. 4, pp. 623-630, 2009, doi: 10.1140/epjb/e2009-00335-8.
[20] M.E. Newman, “Clustering and preferential attachment in growing networks,” Phys. Rev. E, vol. 64, no. 2, p. 025102, 2001, doi: 10.1103/PhysRevE.64.025102.
[21] N. Benchettara, R. Kanawati, and C. Rouveirol, “A supervised machine learning link prediction approach for academic collaboration recommendation,” in Proc. 4th ACM Conf. Recommender Syst., 2010, pp. 253-256, doi: 10.1145/1864708.1864760.
[22] F. Almeida and G. Xexeo, “Word embeddings: A survey,” arXiv preprint arXiv:1901.09069, 2019.
[23] J. Pennington, R. Socher, and C.D. Manning, “GloVe: Global vectors for word representation,” in Proc. Conf. Empirical Methods Nat. Lang. Process. (EMNLP), 2014, pp. 1532-1543.
[24] J. Zhou, L. Liu, W. Wei, and J. Fan, “Network representation learning: From preprocessing, feature extraction to node embedding,” ACM Comput. Surv., vol. 55, no. 2, pp. 1-35, 2022.
[25] M. Grohe, “word2vec, node2vec, graph2vec, x2vec: Towards a theory of vector embeddings of structured data,” in Proc. 39th ACM SIGMOD-SIGACT-SIGAI Symp. Princ. Database Syst., 2020, pp. 1-16.
[26] A. Grover and J. Leskovec, “node2vec: Scalable feature learning for networks,” in Proc. 22nd ACM SIGKDD Int. Conf. Knowl. Discov. Data Mining, 2016, pp. 855-864, doi: 10.1145/2939672.2939754.
[27] D. Lande, M. Fu, W. Guo, I. Balagura, I. Gorbov, and H. Yang, “Link prediction of scientific collaboration networks based on information retrieval,” World Wide Web, vol. 23, pp. 1-19, 2020, doi: 10.1007/s11280-019-00768-9.
[28] B. Liu, S. Xu, T. Li, J. Xiao, and X.-K. Xu, “Quantifying the effects of topology and weight for link prediction in weighted complex networks,” Entropy, vol. 20, no. 5, p. 363, 2018, doi: 10.3390/e20050363.
[29] A. Hagberg, P.J. Swart, and D.A. Schult, “Exploring network structure, dynamics, and function using NetworkX,” Los Alamos National Lab (LANL), Los Alamos, NM, USA, Rep. LA-UR-08-05495, 2008.