Abstract
Link prediction problem has increasingly become prominent in many domains such as social network analyses, bioinformatics experiments, transportation networks, criminal investigations and so forth. A variety of techniques has been developed for link prediction problem, categorized into (1) similarity-based approaches which study a set of features to extract similar nodes; (2) learning-based approaches which extract patterns from the input data; (3) probabilistic statistical approaches which optimize a set of parameters to establish a model which can best compute formation probability. However, existing literatures lack approaches which utilize strength of each approach by integrating them to achieve a much more productive one. To tackle the link prediction problem, we propose an approach based on the combination of first and second group methods; the existing studied works use just one of these categories. Our two-phase developed method firstly determines new features related to the position and dynamic behavior of nodes, which enforce the approach more efficiency compared to approaches using mere measures. Then, a subspace clustering algorithm is applied to group social objects based on the computed similarity measures which differentiate the strength of clusters; basically, the usage of local and global indices and the clustering information plays an imperative role in our link prediction process. Some extensive experiments held on real datasets including Facebook, Brightkite and HepTh indicate good performances of our proposal method. Besides, we have experimentally verified our approach with some previous techniques in the area to prove the supremacy of ours.
Similar content being viewed by others
References
Ahmed, R. A.: The network data repository with interactive graph analytics and visualization. In: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, pp. 4292–4293 (2015)
Al Hasan, M., Zaki, M.J.: A survey of link prediction in social networks. In: Social Network Data Analytics, pp. 243–275. Springer, Boston (2011)
Almansoori, W., Gao, S., Jarada, T.N., ElSheikh, A.M., Murshed, A.N., Jida, J., et al.: Link prediction and classification in social networks and its application in healthcare and systems biology. Netw. Model. Anal. Health Inform. Bioinform. 1(1–2), 27–36 (2012)
Aziz, F., Gul, H., Uddin, I., Gkoutos, G.V.: Path-based extensions of local link prediction methods for complex networks. Sci. Rep. 10(1), 1–11 (2020)
Backstrom, L.: Supervised random walks: predicting and recommending links in social networks. In:Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, pp. pp. 635–644. ACM (2007)
Barabási, A.L.: Emergence of scaling in random networks. Science 286(5439), 509–512 (1999)
Bastami, E., Mahabadi, A., Taghizadeh, E.: A gravitation-based link prediction approach in social networks. Swarm Evol. Comput. 44, 176–186 (2019)
Berlusconi, G., Calderoni, F., Parolini, N., Verani, M., Piccardi, C.: Link prediction in criminal networks: A tool for criminal intelligence analysis. PLoS ONE 11(4), e0154244 (2016)
Bliss, C.A.: An evolutionary algorithm approach to link prediction in dynamic social networks. J. Comput. Sci. 5(5), 750–764 (2014)
Buchegger, S., Datta, A.: A case for P2P infrastructure for social networks-opportunities & challenges. In: Sixth International Conference on Wireless On-Demand Network Systems and Services, pp. 161–168. IEEE (2009)
Burt, R.S.: Social network analysis: foundations and frontiers on advantage. Annu. Rev. Psychol. 64, 527–547 (2013)
Cai, S.: Balance between complexity and quality: Local search for minimum vertex cover in massive graphs. In: Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI, pp. 25–31 (2015)
Calderoni, F., Catanese, S., Meo, P.D., Ficara, A., Fiumara, G.: Robust link prediction in criminal networks: A case study of the Sicilian Mafia. Expert Syst. Appl. 161, 113666 (2020)
Chen, B., a. : A link prediction algorithm based on ant colony optimization. Appl. Intell. 41(3), 694–708 (2014)
Cho, E.M.: Friendship and mobility: user movement in location-based social networks. In:Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1082–1090. ACM (2011)
Dong, Y., Ke, Q., Wang, B., Wu, B.: Link prediction based on local information. In: 2011 International Conference on Advances in Social Networks Analysis and Mining, pp. 382–386. IEEE (2011).
Duan, L., Ma, S., Aggarwal, C., Ma, T., Huai, J.: An ensemble approach to link prediction. IEEE Trans. Knowl. Data Eng. 29(11), 2402–2416 (2017)
Gaudelet, T., Day, B., Jamasb, A. R., Soman, J., Regep, C., Liu, G., et al.: Utilising Graph Machine Learning within Drug Discovery and Development (2020). arXiv:2012.05716.
Gu, S., Chen, L., Li, B., Liu, W., Chen, B.: Link prediction on signed social networks based on latent space mapping. Appl. Intell. 49(2), 703–722 (2019)
Haghani, S., Keyvanpour, M.R.: A systemic analysis of link prediction in social network. Artif. Intell. Rev. 52(3), 1961–1995 (2019)
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. ACM SIGKDD Explor. Newsl. 11(1), 10–18 (2009)
Hong, L. A. (2012). Discovering geographical topics in the twitter stream. In: Proceedings of the 21st International Conference on World Wide Web, pp. 769–778. ACM.
Ilyas, I.F.: A survey of top-k query processing techniques in relational database systems. ACM Comput. Surv. (CSUR) 40(4), 11 (2008)
Islam, M. K., Aridhi, S., & Smail-Tabbone, M.: A comparative study of similarity-based and GNN-based link prediction approaches (2020). arXiv:2008.08879.
Kégl, B.: The return of AdaBoost. MH: multi-class Hamming trees (2013). arXiv preprint arXiv.
Klimek, P., Jovanovic, A.S., Egloff, R., Schneider, R.: Successful fish go with the flow: citation impact prediction based on centrality measures for term–document networks. Scientometrics 107(3), 1265–1282 (2016)
Kohavi, R.: A study of cross-validation and bootstrap for accuracy estimation and model selection. In: IJCAI, pp. 1137–1145 (1995)
Kossinets, G.: Effects of missing data in social networks. Social networks 28(3), 247–268 (2006)
Li, J., Zhang, L., Meng, F., Li, F.: Recommendation algorithm based on link prediction and domain knowledge in retail transactions. Procedia Computer Science 31, 875–881 (2014)
Liben-Nowell, D., Kleinberg, J.: The link-prediction problem for social networks. J. Am. Soc. Inform. Sci. Technol. 58(7), 1019–1031 (2007)
Lichtenwalter, R.N., Lussier, J.T., Chawla, N.V.: New perspectives and methods in link prediction. In:Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 243–252. ACM (2010)
Lim, M., Abdullah, A., Jhanjhi, N.Z., Khan, M.K., Supramaniam, M.: Link prediction in time-evolving criminal network with deep reinforcement learning technique. IEEE Access. 7, 184797–184807 (2019)
Lü, L.M.: Recommender systems. Phys. Rep. 516, 1–49 (2012)
Lü, L., Zhou, T.: Link prediction in complex networks: a survey. Physica A 390(6), 1150–1170 (2011)
Martínez, V., Berzal, F., Cubero, J.-C.: A survey of link prediction in complex networks. ACM Comput. Surv. (CSUR) 49(4), 69 (2017)
Mutlu, E.C., Oghaz, T.A.: Review on graph feature learning and feature extraction techniques for link prediction (2019). arXiv:1901.03425.
Nechaev, Y., Corcoglioniti, F., Giuliano, C.: SocialLink: exploiting graph embeddings to link DBpedia entities to Twitter profiles. Prog. Artif. Intell. 7(4), 251–272 (2018)
Pan, Y., Li, D.-H., Liu, J.-G., Liang, J.-Z.: Detecting community structure in complex networks via node similarity. Physica A 389(14), 2849–2857 (2010)
Peng, W., BaoWen, X., YuRong, W., XiaoYu, Z.: Link prediction in social networks: the state-of-the-art. Sci. China Inf. Sci. 58(1), 1–38 (2015)
Pittala, S., Koehler, W., Deans, J., Salinas, D., Bringmann, M., Volz, K. S., et al.: Relation-weighted link prediction for disease gene identification (2020). preprint arXiv, 2011.05138 arXiv:2011.05138.
Raut, P., Khandelwal, H., & Vyas, G.: A comparative study of classification algorithms for link prediction. In: 2020 2nd International Conference on Innovative Mechanisms for Industry Applications (ICIMIA). IEEE
S, H., & Sadasivam G, S. : A review of similarity measures and link prediction models in social networks. Int. J. Comput. Digital Syst. 9(2), 239–248 (2020)
Samad, A., Qadir, M., Nawaz, I.: Sam: a similarity measure for link prediction in social network. In: 2019 13th International Conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS), pp. 1–9. IEEE
Samad, A., Qadir, M., Nawaz, I., Islam, M.A., Aleem, M.: A comprehensive survey of link prediction techniques for social network. In: EAI Endorsed Transactions Industrial Networks and Intelligent Systems 7, no. 23 (2020)
Sarkar, P.C. (2011). Theoretical justification of popular link prediction heuristics. In: IJCAI Proceedings-International Joint Conference on Artificial Intelligence, vol 22, no. 3, p. 2722
Sengupta, D.S.: GraphReduce: processing large-scale graphs on accelerator-based systems. In:Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, p. 28. ACM (2015)
Sharma, R., Datta, A., DeH'Amico, M., Michiardi, P.: An empirical study of availability in friend-to-friend storage systems. In: 2011 IEEE International Conference on Peer-to-Peer Computing (pp. 348–351). IEEE.
Sharma, U., Minocha, B. (2016). Link prediction in social networks: a similarity score based neural network approach. In:Proceedings of the Second International Conference on Information and Communication Technology for Competitive Strategies, pp. 1–5. ACM
Sherkat, E.M.: Structural link prediction based on ant colony approach in social networks. Physica A 419, 80–94 (2015)
Silva, N.B., Tsang, R., Cavalcanti, G.D., Tsang, J.: A graph-based friend recommendation system using genetic algorithm. In: IEEE Congress on Evolutionary Computation, pp. 1–7. IEEE (2010)
Stanfield, Z., Coşkun, M., Koyutürk, M.: Drug response prediction as a link prediction problem. Sci. Rep. 7(1), 1–13 (2017)
Symeonidis, P., Tiakas, E., Manolopoulos, Y.: Transitive node similarity for link prediction in social networks with positive and negative links. In: Proceedings of the Fourth ACM Conference on Recommender Systems, pp. 183–190. ACM
Turki, T., Wang, J.T. (2015). A new approach to link prediction in gene regulatory networks. In: International Conference on Intelligent Data Engineering and Automated Learning, pp. 404–415. Springer, Cham
Wu, X., Kumar, V., Quinlan, J.R., Ghosh, J., Yang, Q., Motoda, H., et al.: Top 10 algorithms in data mining. Knowl. Inf. Syst. 14(1), 1–37 (2008)
Xie, F., Chen, Z., Shang, J., Feng, X., Li, J.: A link prediction approach for item recommendation with complex number. Knowl.-Based Syst. 81, 148–158 (2015)
Yaghi, R.I., Faris, H., Aljarah, I., Ala’M, A.-Z., Heidari, A.A., & Mirjalili, S. (2020). Link prediction using evolutionary neural network models. In: Evolutionary Machine Learning Techniques, pp. 85–111
Yuan, H., Ma, Y., Zhang, F., Liu, M., & Shen, W.: A distributed link prediction algorithm based on clustering in dynamic social networks. In: Systems, Man, and Cybernetics (SMC), 2015 IEEE International Conference, pp. 1341–1345. IEEE (2015)
Yuan, W., He, K., Guan, D., Zhou, L., Li, C.: Graph kernel based link prediction for signed social networks. Inf. Fusion 46, 1–10 (2019)
Zarco, C., Santos, E., Cordón, O.: Advanced visualization of Twitter data for its analysis as a communication channel in traditional companies. Prog. Artif. Intell. 8(3), 307–323 (2019)
Zhang, M., Chen, Y.: Link prediction based on graph neural networks (2018). arXiv:1802.09691.
Zhao, Z., Feng, S., Wang, Q., Huang, J.Z., Williams, G.J., Fan, J.: Topic oriented community detection through social objects and link analysis in social networks. Knowl.-Based Syst. 26, 164–173 (2012)
Zhou, T., Lü, L., Zhang, Y.-C.: Predicting missing links via local information. The European Physical Journal B 71(4), 623–630 (2009)
Zhu, C., Li, J., Somasundaram, S.: Global Link Prediction for E-commerce using Deep Networks (2019). Retrieved from snap.stanford.edu
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Ghasemi, S., Zarei, A. Improving link prediction in social networks using local and global features: a clustering-based approach. Prog Artif Intell 11, 79–92 (2022). https://doi.org/10.1007/s13748-021-00261-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13748-021-00261-3