Abstract
The problem of co-authors selection in the area of scientific collaborations might be a daunting one. In this paper, we propose a new pipeline that effectively utilizes citation data in the link prediction task on the co-authorship network. In particular, we explore the capabilities of a recommender system based on data aggregation strategies on different graphs. Since graph neural networks proved their efficiency on a wide range of tasks related to recommendation systems, we leverage them as a relevant method for the forecasting of potential collaborations in the scientific community.
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References
Alinani, K., Wang, G., Alinani, A., Narejo, D.H.: Who should be my co-author? recommender system to suggest a list of collaborators. In: 2017 IEEE International Symposium on Parallel and Distributed Processing with Applications and 2017 IEEE International Conference on Ubiquitous Computing and Communications (ISPA/IUCC), pp. 1427–1433. IEEE (2017)
Bojanowski, P., Grave, E., Joulin, A., Mikolov, T.: Enriching word vectors with subword information. Trans. Assoc. Comput. Linguist. 5, 135–146 (2017)
Chuan, P.M., Son, L.H., Ali, M., Khang, T.D., Huong, L.T., Dey, N.: Link prediction in co-authorship networks based on hybrid content similarity metric. Appl. Intell. 48(8), 2470–2486 (2017). https://doi.org/10.1007/s10489-017-1086-x
Grover, A., Leskovec, J.: node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 855–864 (2016)
Gupta, C., Jain, Y., De, A., Chakrabarti, S.: Integrating transductive and inductive embeddings improves link prediction accuracy. arXiv preprint arXiv:2108.10108 (2021)
Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017)
Leskovec, J., Kleinberg, J., Faloutsos, C.: Graph evolution: densification and shrinking diameters. ACM Trans. Knowl. Discovery Data 1 (2006)
Liu, W., Lü, L.: Link prediction based on local random walk. EPL (Europhys. Lett.) 89(5), 58007 (2010)
Liu, Z., Zhang, Q.M., Lü, L., Zhou, T.: Link prediction in complex networks: a local naïve bayes model. EPL (Europhys. Lett.) 96(4), 48007 (2011)
Makarov, I., Bulanov, O., Gerasimova, O., Meshcheryakova, N., Karpov, I., Zhukov, L.E.: Scientific matchmaker: collaborator recommender system. In: van der Aalst, W.M.P., et al. (eds.) AIST 2017. LNCS, vol. 10716, pp. 404–410. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-73013-4_37
Makarov, I., Bulanov, O., Zhukov, L.E.: Co-author recommender system. In: Kalyagin, V.A., Nikolaev, A.I., Pardalos, P.M., Prokopyev, O.A. (eds.) NET 2016. SPMS, vol. 197, pp. 251–257. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-56829-4_18
Makarov, I., Gerasimova, O., Sulimov, P., Korovina, K., Zhukov, L.E.: Joint node-edge network embedding for link prediction. In: van der Aalst, W.M.P., et al. (eds.) Analysis of Images, Social Networks and Texts, pp. 20–31. Springer International Publishing, Cham (2018)
Sie, R.L., Drachsler, H., Bitter-Rijpkema, M., Sloep, P.: To whom and why should i connect? co-author recommendation based on powerful and similar peers. Int. J. Technol. Enhanced Learn. 4(1–2), 121–137 (2012)
Singh, A., et al.: Edge proposal sets for link prediction. arXiv preprint arXiv:2106.15810 (2021)
Ying, R., He, R., Chen, K., Eksombatchai, P., Hamilton, W.L., Leskovec, J.: Graph convolutional neural networks for web-scale recommender systems. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 974–983. KDD 2018, Association for Computing Machinery, New York, NY, USA (2018). https://doi.org/10.1145/3219819.3219890
Zhang, D., Yin, J., Zhu, X., Zhang, C.: Attributed network embedding via subspace discovery. Data Min. Knowl. Disc. 33(6), 1953–1980 (2019). https://doi.org/10.1007/s10618-019-00650-2
Zhang, M., Chen, Y.: Link prediction based on graph neural networks. Adv. Neural. Inf. Process. Syst. 31, 5165–5175 (2018)
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We acknowledge fruitful discussions with Natalia Semenova.
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Tishin, V., Sosedka, A., Ibragimov, P., Porvatov, V. (2022). Citation Network Applications in a Scientific Co-authorship Recommender System. In: Burnaev, E., et al. Analysis of Images, Social Networks and Texts. AIST 2021. Lecture Notes in Computer Science, vol 13217. Springer, Cham. https://doi.org/10.1007/978-3-031-16500-9_24
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DOI: https://doi.org/10.1007/978-3-031-16500-9_24
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