Abstract
Network representation learning can transform nodes into a low-dimensional vector representation, which has been widely used in recent years. As a heterogeneous information network, the academic network contains more information than the homogeneous information network, which attracts many scholars to do research. Most of the existing algorithms cannot make full use of the attribute or text information of nodes, which contains more information than structural features. To solve this problem, we propose a network representation learning algorithm based on research interest and meta-path. Firstly, the author’s research interest are extracted from their published papers, and then the random walk based on meta-path is used. The author node is transformed into several research interest nodes by mapping function, and the skip-gram model is used to train and get the vector representation of the nodes. Experiments show that our model outperforms traditional learning models in several heterogeneous network analysis tasks, such as node classification and similarity search. The obtained research interest representation can help solve the cold start problem of authors in the academic network.
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Acknowledgments
The work in this paper is supported by the National Key Research and Development Plan (2018YFB1004700, 2016YFB0800403).
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Zhang, W., Liang, Y., Dong, X. (2019). Representation Learning in Academic Network Based on Research Interest and Meta-path. In: Douligeris, C., Karagiannis, D., Apostolou, D. (eds) Knowledge Science, Engineering and Management. KSEM 2019. Lecture Notes in Computer Science(), vol 11776. Springer, Cham. https://doi.org/10.1007/978-3-030-29563-9_34
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DOI: https://doi.org/10.1007/978-3-030-29563-9_34
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