Abstract
Predicting authors’ academic behavior (e.g. co-authorship, citation) based on heterogeneous academic network can help scholars to grasp interesting research directions and participate in various co-operations. Most of the existing network representation methods use the structural and content features of nodes, but have not fully exploited the edges (relationships) between nodes (entities) and investigated the semantic compatibility of different edge types yet. To solve the above problems, a heterogeneous network representation learning method (HNEABP) is proposed to improve feature extraction and academic behavior prediction performance. HNEABP has three strengths: 1) capture rich neighbor information via balanced sampling and Skip-Gram, 2) apply knowledge graph embedding (KGE) technique to learn pairwise node information and to weight the importance of first-order neighbors, 3) solve the semantic incompatibility of edges based on KGE. Validation experiments on three academic network datasets show that HNEABP outperforms the popular network representation methods, which gives the credit to HNEABP for learning richer feature information effectively, so as to improve the performance of academic behavior prediction.
Keywords
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Perozzi B., Al-rfou R., Skiena S.: DeepWalk: online learning of social representations. In: Proceedings of the 20th ACM SIGKDD, New York, USA, pp. 701–710 (2014)
Grover A., Leskovec J.: Node2vec: scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD, New York, USA, pp. 855–864 (2017)
Hussein R., Yang D., Cudré-Mauroux P.: Are meta-paths necessary? Revisiting heterogeneous graph embeddings. In: Proceedings of the 27th ACM CIKM, Torino, UK, pp.437–446 (2018)
Lee, S., Park, C., Yu, H.: BHIN2vec: balancing the type of relation in heterogeneous information network. In: Proceedings of the 28th ACM CIKM, Beijing, China, pp.619–628 (2019)
Lin, Y., Liu, Z., Sun, M., et al.: Learning entity and relation embeddings for knowledge graph completion. In: Twenty-Ninth AAAI Conference, Texas, USA, pp. 2181–2187 (2015)
Zhao, J., Wang, X., et al.: Network schema preserved heterogeneous information network embedding. In: 29th IJCAI, Yokohama, Japan, pp. 1366–1372 (2020)
Chen, H., Yin, H., Wang, W., et al.: PME: projected metric embedding on heterogeneous networks for link prediction. In: Proceedings of the 24th ACM SIGKDD, London, UK, pp. 1177–1186 (2018)
Shi, Y., Zhu, Q., Guo, F., et al.: Easing embedding learning by comprehensive transcription of heterogeneous information networks. In: Proceedings of the 24th ACM SIGKDD, London, UK, pp. 2190–2199 (2018)
Shi, Y., Gui, H., Zhu, Q., et al.: Embedding learning by aspects in heterogeneous information networks. In: Proceedings of the SIAM, California, USA, pp. 144–152 (2018)
Chairatanakul, N., Liu, X., et al.: PGRA: projected graph relation-feature attention network for heterogeneous information network embedding. Inf. Sci. 570, 769–794 (2021)
Lu, Y., Shi, C., Hu, L., et al.: Relation structure-aware heterogeneous information network embedding. In: AAAI, Hawaii, USA, pp. 4456–4463 (2019)
Dong, Y., Chawla, N., Swami, A.: Metapath2vec: scalable representation learning for heterogeneous networks. In: Proceedings of the 23rd ACM SIGKDD, Halifax, Canada, pp. 135–144 (2017)
Acknowledgments
This work is supported by the Sichuan Science and Technology Program (No 2019YFSY0032).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Huang, L., Zhu, Y. (2022). A Heterogeneous Network Representation Learning Approach for Academic Behavior Prediction. In: Strauss, C., Cuzzocrea, A., Kotsis, G., Tjoa, A.M., Khalil, I. (eds) Database and Expert Systems Applications. DEXA 2022. Lecture Notes in Computer Science, vol 13426. Springer, Cham. https://doi.org/10.1007/978-3-031-12423-5_20
Download citation
DOI: https://doi.org/10.1007/978-3-031-12423-5_20
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-12422-8
Online ISBN: 978-3-031-12423-5
eBook Packages: Computer ScienceComputer Science (R0)