Abstract
With the rapid development of Internet technology and academic social media, it has become one of the important research directions of social networks to measure the similarity between scholars, which enables users to find scholars with similar research interests or similar backgrounds in the scholars’ network and promote scientific research cooperation and communication. In this paper, we propose an algorithm for measuring the similarity of scholars based on variational graph normalized auto-encoders (VGNAE), which fuses the social relationship and attribute information of scholars, obtains the implicit feature representation of scholars through graph convolutional neural network encoding, and then compares different implicit feature vectors to measure the similarity of different scholars. In order to evaluate the effectiveness of this method, the SCHOLAT social network dataset is selected for experiments in this paper. The experimental results show that the model achieves the best performance on both the AUC and AP metrics on the task of measuring scholar similarity, with 98.7% and 98.8%, respectively, relative to other traditional widely used algorithms. In addition, by fusing the scholar similarity information into the scholar network visualization, users can access the connection between different scholars more intuitively and efficiently.
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References
Khoshraftar, S., An, A.: A survey on graph representation learning methods. arXiv preprint arXiv:2204.01855 (2022)
Wu, L., Cui, P., Pei, J., et al.: Graph neural networks: foundation, frontiers and applications. In: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 4840–4841 (2022)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)
Veličković, P., Cucurull, G., Casanova, A., et al.: Graph attention networks. arXiv preprint arXiv:1710.10903 (2017)
Kipf, T.N., Welling, M.: Variational graph auto-encoders. arXiv preprint arXiv:1611.07308 (2016)
Ilic, M.: Auto-encoding variational Bayes (2019)
Ahn, S.J., Kim, M.H.: Variational graph normalized autoencoders. In: Proceedings of the 30th ACM International Conference on Information and Knowledge Management, pp. 2827–2831 (2021)
Ko, H., Lee, S., Park, Y., et al.: A survey of recommendation systems: recommendation models, techniques, and application fields. Electronics 11(1), 141 (2022)
Lv, M., Chen, L., Chen, G.: Mining user similarity based on routine activities. Inf. Sci. 236, 17–32 (2013)
Zhang, X., Yu, H., Fan, G.: A user recommendation method based on similarity metric. Comput. Eng. Des. 7 (2020)
Suhaim, A.B., Berri, J.: Directional user similarity model for personalized recommendation in online social networks. J. King Saud Univ.-Comput. Inf. Sci. 34(10), 10205–10216 (2022)
Kunlun, L., Jiayao, Z., Mengmeng, W., et al.: A recommendation algorithm combining semi-supervised AP clustering and improved similarity. Small Microcomput. Syst. 42(7), 1396–1401 (2021)
Ai, J., Cai, Y., Su, Z., et al.: Predicting user-item links in recommender systems based on similarity-network resource allocation. Chaos Solitons Fractals 158, 112032 (2022)
Hawashin, B., Lafi, M., Kanan, T., et al.: An efficient hybrid similarity measure based on user interests for recommender systems. Expert. Syst. 37(5), e12471 (2020)
Su, Z., Zheng, X., Ai, J., et al.: Link prediction in recommender systems based on vector similarity. Physica A 560, 125154 (2020)
Bank, D., Koenigstein, N., Giryes, R.: Autoencoders. arXiv preprint arXiv:2003.05991 (2020)
Wu, Z., Pan, S., Chen, F., et al.: A comprehensive survey on graph neural networks. IEEE Trans. Neural Netw. Learn. Syst. 32(1), 4–24 (2020)
Zhang, J., Lin, N., Zhang, X., et al.: Learning concept prerequisite relations from educational data via multi-head attention variational graph auto-encoders. In: Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining, 1377–1385 (2022)
Ding, Y., Tian, L.P., Lei, X., et al.: Variational graph auto-encoders for miRNA-disease association prediction]. Methods 192, 25–34 (2021)
De Boer, P.-T., et al.: A tutorial on the cross-entropy method. Ann. Oper. Res. 134(1), 19–67 (2005)
Hershey, J.R., Olsen, P.A.: Approximating the KullbackLeibler divergence between Gaussian mixture models. In: 2007 IEEE International (2007)
Jie-yang, J., Zhao, Y., Ting, Z., et al.: Deep learning radiomics model accurately predicts hepatocellular carcinoma occurrence in chronic hepatitis B patients: a five-year follow-up. Am. J. Cancer Res. 11(2), 576–589 (2021)
Ahmad, I., Akhtar, M.U., Noor, S., et al.: Missing link prediction using common neighbor and centrality based parameterized algorithm. Sci. Rep. 10, 364 (2020). https://doi.org/10.1038/s41598-019-57304-y
Liben-Nowell, D., Kleinberg, J.: The Link Prediction Problem for Social Networks (2004)
Zhou, T., Lü, L., Zhang, Y.C.: Predicting missing links via local information. Eur. Phys. J. B 71, 623–630 (2009)
Gasteiger, J., Bojchevski, A., GĂĽnnemann, S.: Predict then propagate: graph neural networks meet personalized pagerank. arXiv preprint arXiv:1810.05997 (2018)
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Our works were supported by the National Natural Science Foundation of China (No. 62377015).
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Zhang, G., Zeng, X., Weng, Y., Wu, Z. (2024). Similarity Metrics and Visualization of Scholars Based on Variational Graph Normalized Auto-Encoders. In: Sun, Y., Lu, T., Wang, T., Fan, H., Liu, D., Du, B. (eds) Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2023. Communications in Computer and Information Science, vol 2012. Springer, Singapore. https://doi.org/10.1007/978-981-99-9637-7_5
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