Abstract
The work is devoted to academic papers recommendation task considered as link prediction on a static citation network. We compare several graph embeddings, text-based and fusion models in the link prediction problem on academic papers citation dataset. We showed that fusion models of graph and text information outperform other approaches based on graph or text information alone. We prove this via an extensive set of experiments with different train/test splits that our fusion models are robust and retain superior performance even with a reduced train set.
The article was prepared within the framework of the HSE University Basic Research Program.
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References
Abu-El-Haija, S., Perozzi, B., Al-Rfou, R.: Learning edge representations via low-rank asymmetric projections. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pp. 1787–1796 (2017)
Beel, J., Gipp, B., Langer, S., Breitinger, C.: Paper recommender systems: a literature survey. Int. J. Digit. Libr. 17(4), 305–338 (2016)
Belkin, M., Niyogi, P.: Laplacian eigenmaps and spectral techniques for embedding and clustering. In: NIPS, vol. 14, pp. 585–591 (2001)
Bethard, S., Jurafsky, D.: Who should I cite: learning literature search models from citation behavior. In: Proceedings of IC CIKM, pp. 609–618 (2010)
Cao, S., Lu, W., Xu, Q.: GraRep: learning graph representations with global structural information. In: Proceedings of IC CIKM, pp. 891–900 (2015)
Cao, S., Lu, W., Xu, Q.: Deep neural networks for learning graph representations. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 30 (2016)
Chen, H., Perozzi, B., Hu, Y., Skiena, S.: Harp: hierarchical representation learning for networks. In: Proceedings of AAAI, vol. 32 (2018)
Gabrilovich, E., Markovitch, S., et al.: Computing semantic relatedness using Wikipedia-based explicit semantic analysis. In: IJcAI, vol. 7, pp. 1606–1611 (2007)
Gerasimova, O., Makarov, I.: Higher school of economics co-authorship network study. In: 2019 2nd International Conference on Computer Applications & Information Security (ICCAIS), pp. 1–4. IEEE (2019)
Gerasimova, O., Syomochkina, V.: Linking friends in social networks using HashTag attributes. In: van der Aalst, W.M.P., et al. (eds.) AIST 2020. LNCS, vol. 12602, pp. 269–281. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-72610-2_20
Giles, C.L., Bollacker, K.D., Lawrence, S.: Citeseer: an automatic citation indexing system. In: Proceedings of JCDL, pp. 89–98 (1998)
Gomaa, W.H., Fahmy, A.A., et al.: A survey of text similarity approaches. Int. J. Comput. Appl. 68(13), 13–18 (2013)
Goyal, P., Ferrara, E.: Graph embedding techniques, applications, and performance: a survey. Knowl.-Based Syst. 151, 78–94 (2018)
Grover, A., Leskovec, J.: Node2vec: scalable feature learning for networks. In: Proceedings of ACM SIGKDD, pp. 855–864 (2016)
Harris, Z.: Distributional structure. Word 10(2–3), 146–162 (1954)
Hou, C., He, S., Tang, K.: Attributed network embedding for incomplete attributed networks. arXiv:1811.11728 (2018)
Joulin, A., Grave, E., Bojanowski, P., Douze, M., Jégou, H., Mikolov, T.: FastText. zip: compressing text classification models. arXiv:1612.03651 (2016)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv:1609.02907 (2016)
Kipf, T.N., Welling, M.: Variational graph auto-encoders. arXiv:1611.07308 (2016)
Kusner, M., Sun, Y., Kolkin, N., Weinberger, K.: From word embeddings to document distances. In: ICML, pp. 957–966. PMLR (2015)
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.: Link prediction regression for weighted co-authorship networks. In: Rojas, I., Joya, G., Catala, A. (eds.) IWANN 2019. LNCS, vol. 11507, pp. 667–677. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-20518-8_55
Makarov, I., Gerasimova, O.: Predicting collaborations in co-authorship network. In: 2019 14th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP), pp. 1–6. IEEE (2019)
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.) AIST 2018. LNCS, vol. 11179, pp. 20–31. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-11027-7_3
Makarov, I., Gerasimova, O., Sulimov, P., Zhukov, L.E.: Recommending co-authorship via network embeddings and feature engineering: the case of national research university higher school of economics. In: Proceedings of the 18th ACM/IEEE on Joint Conference on Digital Libraries, pp. 365–366. ACM (2018)
Makarov, I., Gerasimova, O., Sulimov, P., Zhukov, L.E.: Dual network embedding for representing research interests in the link prediction problem on co-authorship networks. PeerJ Comput. Sci. 5, e172 (2019)
Makarov, I., Kiselev, D., Nikitinsky, N., Subelj, L.: Survey on graph embeddings and their applications to machine learning problems on graphs. PeerJ Comput. Sci. 7, e357 (2021)
Makarov, I., Korovina, K., Kiselev, D.: JONNEE: joint network nodes and edges embedding. IEEE Access 9, 1–14 (2021)
Makarov, I., Makarov, M., Kiselev, D.: Fusion of text and graph information for machine learning problems on networks. PeerJ Comput. Sci. 7, e526 (2021)
McNee, S.M., et al.: On the recommending of citations for research papers. In: Proceedings of the 2002 ACM Conference on Computer Supported Cooperative Work, pp. 116–125 (2002)
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv:1301.3781 (2013)
Ou, M., Cui, P., Pei, J., Zhang, Z., Zhu, W.: Asymmetric transitivity preserving graph embedding. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1105–1114 (2016)
Page, L., Brin, S., Motwani, R., Winograd, T.: The PageRank citation ranking: bringing order to the web. Technical report, Stanford InfoLab (1999)
Perozzi, B., Al-Rfou, R., Skiena, S.: DeepWalk: online learning of social representations. In: Proceedings of ACM SIGKDD, pp. 701–710 (2014)
Radev, D.R., Muthukrishnan, P., Qazvinian, V., Abu-Jbara, A.: The ACL anthology network corpus. Lang. Resour. Eval. 47(4), 919–944 (2013)
Shaw, B., Jebara, T.: Structure preserving embedding. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 937–944 (2009)
Strohman, T., Croft, W.B., Jensen, D.: Recommending citations for academic papers. In: Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 705–706 (2007)
Sun, K., Zhong, S., Xu, H.: Learning embeddings of directed networks with text-associated nodes-with application in software package dependency networks. In: 2020 IEEE International Conference on Big Data (Big Data), pp. 2995–3004. IEEE (2020)
Tian, H., Zhuo, H.H.: Paper2vec: citation-context based document distributed representation for scholar recommendation. arXiv:1703.06587 (2017)
Wang, D., Cui, P., Zhu, W.: Structural deep network embedding. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1225–1234 (2016)
Yang, C., Liu, Z., Zhao, D., Sun, M., Chang, E.: Network representation learning with rich text information. In: Twenty-Fourth International Joint Conference on Artificial Intelligence (2015)
Yang, C., Wei, B., Wu, J., Zhang, Y., Zhang, L.: Cares: a ranking-oriented CADAL recommender system. In: Proceedings of the 9th ACM/IEEE-CS Joint Conference on Digital Libraries, pp. 203–212 (2009)
Yu, X., Gu, Q., Zhou, M., Han, J.: Citation prediction in heterogeneous bibliographic networks. In: Proceedings of the 2012 SIAM International Conference on Data Mining, pp. 1119–1130. SIAM (2012)
Zarrinkalam, F., Kahani, M.: SemCiR. Program (2013)
Zhang, Z., Cui, P., Zhu, W.: Deep learning on graphs: a survey. IEEE Trans. Knowl. Data Eng. (2020)
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Gerasimova, O., Lapidus, A., Makarov, I. (2022). Research Papers Recommendation. 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_22
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