A Neighbor-based Link Prediction Method for Bipartite Networks
Subject Areas :Golshan Sondossi 1 , alireza saebi 2 , S. Alireza hashemi G. 3
1 - University student
2 - University student
3 - Academic staff
Keywords:
Abstract :
Social network analysis’ link prediction has a diverse range of applications in different areas of science. Bipartite networks are a kind of complex network, which can be used to describe various real-world phenomena. In this article, a link prediction method for bipartite network is presented. Uni-partite link prediction methods are not effective and efficient enough to be applied to bipartite networks. Thus, to solve this problem, distinct methods specifically designed for bipartite networks are required. The proposed method is neighbor based and consisted of measures of such. Classic uni-partite link prediction measures are redefined to be compatible with bipartite network. Subsequently, these modified measures are used as the basis of the presented method, which in addition to simplicity, has high performance rates and is superior to other neighbor-based methods by 15% in average.
M. Newman, Networks. Oxford university press, 2018.
[2] A. Alamsyah, “Social network data analytics for market segmentation in indonesian telecommunications industry,” 2017, pp. 1–5.
[3] O. Allali, C. Magnien, and M. Latapy, “Link prediction in bipartite graphs using internal links and weighted projection,” 2011, pp. 936–941.
[4] P. Wang, B. Xu, Y. Wu, and X. Zhou, “Link prediction in social networks: the state-of-the-art,” Science China Information Sciences, vol. 58, no. 1, pp. 1–38, 2015.
[5] V. Martínez, F. Berzal, and J.-C. Cubero, “A survey of link prediction in complex networks,” ACM computing surveys (CSUR), vol. 49, no. 4, pp. 1–33, 2016.
[6] E. Gündoğan and B. Kaya, “A recommendation method based on link prediction in drug-disease bipartite network,” 2017, pp. 125–128.
[7] E. Gündoğan and B. Kaya, “A link prediction approach for drug recommendation in disease-drug bipartite network,” 2017, pp. 1–4.
[8] S. Aslan, B. Kaya, and M. Kaya, “Predicting potential links by using strengthened projections in evolving bipartite networks,” Physica A: Statistical Mechanics and its Applications, vol. 525, pp. 998–1011, 2019.
[9] S. Aslan and B. Kaya, “Time-aware link prediction based on strengthened projection in bipartite networks,” Information Sciences, vol. 506, pp. 217–233, 2020.
[10] Y.-J. Chang and H.-Y. Kao, “Link prediction in a bipartite network using wikipedia revision information,” 2012, pp. 50–55.
[11] S. Xia, B. Dai, E.-P. Lim, Y. Zhang, and C. Xing, “Link prediction for bipartite social networks: The role of structural holes,” 2012, pp. 153–157.
[12] M. Medo, M. S. Mariani, and L. Lü, “Link prediction in bipartite nested networks,” Entropy, vol. 20, no. 10, p. 777, 2018.
[13] C. Zhang, E. Chan, and A. Abdulhamid, “Link prediction in bipartite venture capital investment networks,” CS224-w report, Stanford, 2015.
[14] W. Wang, X. Chen, P. Jiao, and D. Jin, “Similarity-based regularized latent feature model for link prediction in bipartite networks,” Scientific reports, vol. 7, no. 1, pp. 1–12, 2017.
[15] X. Chen, D. Xie, L. Wang, Q. Zhao, Z.-H. You, and H. Liu, “BNPMDA: bipartite network projection for MiRNA–disease association prediction,” Bioinformatics, vol. 34, no. 18, pp. 3178–3186, 2018.
[16] D. Zhao, L. Zhang, and W. Zhao, “Genre-based link prediction in bipartite graph for music recommendation,” Procedia Computer Science, vol. 91, pp. 959–965, 2016.
[17] F. Xie, Z. Chen, J. Shang, X. Feng, and J. Li, “A link prediction approach for item recommendation with complex number,” Knowledge-Based Systems, vol. 81, pp. 148–158, 2015.
[18] Y. Cui, L. Zhang, Q. Wang, P. Chen, and C. Xie, “Heterogeneous network linkage-weight based link prediction in bipartite graph for personalized recommendation,” Procedia Computer Science, vol. 91, pp. 953–958, 2016.
[19] Y. Luo, Q. Liu, W. Wu, F. Li, and X. Bo, “Predicting drug side effects based on link prediction in bipartite network,” 2014, pp. 729–733.
[20] L. Zhang, J. Li, Q. Zhang, F. Meng, and W. Teng, “Domain knowledge-based link prediction in customer-product bipartite graph for product recommendation,” International Journal of Information Technology & Decision Making, vol. 18, no. 01, pp. 311–338, 2019.
[21] M. Koptelov, A. Zimmermann, B. Crémilleux, and L. Soualmia, “Link prediction via community detection in bipartite multi-layer graphs,” 2020, pp. 430–439.
[22] D. B. Larremore, A. Clauset, and A. Z. Jacobs, “Efficiently inferring community structure in bipartite networks,” Physical Review E, vol. 90, no. 1, p. 012805, 2014.
[23] G. Salton and J. Michael, “McGill,” Introduction to modern information retrieval, vol. 1, no. 4.1, pp. 4–1, 1986.
[24] A.-L. Barabâsi, H. Jeong, Z. Néda, E. Ravasz, A. Schubert, and T. Vicsek, “Evolution of the social network of scientific collaborations,” Physica A: Statistical mechanics and its applications, vol. 311, no. 3–4, pp. 590–614, 2002.
[25] T. Zhou, L. Lü, and Y.-C. Zhang, “Predicting missing links via local information,” The European Physical Journal B, vol. 71, no. 4, pp. 623–630, 2009.
[26] E. A. Leicht, P. Holme, and M. E. Newman, “Vertex similarity in networks,” Physical Review E, vol. 73, no. 2, p. 026120, 2006.
[27] L. A. Adamic and E. Adar, “Friends and neighbors on the web,” Social networks, vol. 25, no. 3, pp. 211–230, 2003.