Abstract
Temporal heterogeneous information network (temporal HIN) embedding, aiming to represent various types of nodes of different timestamps into low-dimensional spaces while preserving structural and semantic information, is of vital importance in diverse real-life tasks. Researchers have made great efforts on temporal HIN embedding in Euclidean spaces and got some considerable achievements. However, there is always a fundamental conflict that many real-world networks show hierarchical property and power-law distribution, and are not isometric of Euclidean spaces. Recently, representation learning in hyperbolic spaces has been proved to be valid for data with hierarchical and power-law structure. Inspired by this character, we propose a hyperbolic heterogeneous temporal network embedding (\(\text {H}^2\text {TNE}\)) model for temporal HINs. Specifically, we leverage a temporally and heterogeneously double-constrained random walk strategy to capture the structural and semantic information, and then calculate the embedding by exploiting hyperbolic distance in proximity measurement. Experimental results show that our method has superior performance on temporal link prediction and node classification compared with SOTA models.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Bian, R., Koh, Y.S., Dobbie, G., Divoli, A.: Network embedding and change modeling in dynamic heterogeneous networks. In: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 861–864 (2019)
Bronstein, M.M., Bruna, J., LeCun, Y., Szlam, A., Vandergheynst, P.: Geometric deep learning: going beyond euclidean data. IEEE Signal Process. Mag. 34(4), 18–42 (2017)
Chami, I., Ying, Z., Ré, C., Leskovec, J.: Hyperbolic graph convolutional neural networks. Advances in neural information processing systems 32 (2019)
Chen, F., Wang, Y.C., Wang, B., Kuo, C.C.J.: Graph representation learning: a survey. APSIPA Trans. Sig. Inf. Process. 9 (2020)
Chen, H., Perozzi, B., Al-Rfou, R., Skiena, S.: A tutorial on network embeddings. arXiv preprint arXiv:1808.02590 (2018)
Cui, P., Wang, X., Pei, J., Zhu, W.: A survey on network embedding. IEEE Trans. Knowl. Data Eng. 31(5), 833–852 (2018)
Dong, Y., Chawla, N.V., Swami, A.: metapath2vec: Scalable representation learning for heterogeneous networks. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 135–144 (2017)
Fu, X., Zhang, J., Meng, Z., King, I.: Magnn: Metapath aggregated graph neural network for heterogeneous graph embedding. In: Proceedings of the Web Conference 2020, pp. 2331–2341 (2020)
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)
Guo, J., Bai, Q., Lin, Z., Song, C., Yuan, X.: Dynamic heterogeneous network embedding based on non-decreasing temporal random walk. J. Comput. Res. Dev. 58(8), 1624 (2021)
Harper, F.M., Konstan, J.A.: The movielens datasets: history and context. ACM Trans. Interactive Intell. Syst. (TIIS) 5(4), 1–19 (2015)
Huang, H., Shi, R., Zhou, W., Wang, X., Jin, H., Fu, X.: Temporal heterogeneous information network embedding. In: The 30th International Joint Conference on Artificial Intelligence (2021)
Hussein, R., Yang, D., Cudré-Mauroux, P.: Are meta-paths necessary? revisiting heterogeneous graph embeddings. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management, pp. 437–446 (2018)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: International Conference on Learning Representations (2017)
Krioukov, D., Papadopoulos, F., Kitsak, M., Vahdat, A., Boguná, M.: Hyperbolic geometry of complex networks. Phys. Rev. E 82(3), 036106 (2010)
Lee, J.B., Nguyen, G., Rossi, R.A., Ahmed, N.K., Koh, E., Kim, S.: Dynamic node embeddings from edge streams. IEEE Trans. Emerging Top. Comput. Intell. 5(6), 931–946 (2020)
Li, C., Hu, L., Shi, C., Song, G., Lu, Y.: Sequence-aware heterogeneous graph neural collaborative filtering. In: Proceedings of the 2021 SIAM International Conference on Data Mining (SDM), pp. 64–72. SIAM (2021)
Liu, Q., Nickel, M., Kiela, D.: Hyperbolic graph neural networks. Advances in Neural Information Processing Systems 32 (2019)
Lu, Y., Wang, X., Shi, C., Yu, P.S., Ye, Y.: Temporal network embedding with micro-and macro-dynamics. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, pp. 469–478 (2019)
Nickel, M., Kiela, D.: Poincaré embeddings for learning hierarchical representations. Advances in neural information processing systems 30 (2017)
Nickel, M., Kiela, D.: Learning continuous hierarchies in the lorentz model of hyperbolic geometry. In: International Conference on Machine Learning, pp. 3779–3788. PMLR (2018)
Peng, H., et al..: Dynamic network embedding via incremental skip-gram with negative sampling. Sci. China Inf. Sci. 63(10), 1–19 (2020). https://doi.org/10.1007/s11432-018-9943-9
Peng, H., et al.: Lime: Low-cost incremental learning for dynamic heterogeneous information networks. IEEE Trans. Comput. (2021)
Peng, W., Varanka, T., Mostafa, A., Shi, H., Zhao, G.: Hyperbolic deep neural networks: a survey. IEEE Trans. Pattern Anal. Mach. Intell. (2021)
Perozzi, B., Al-Rfou, R., Skiena, S.: Deepwalk: online learning of social representations. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 701–710 (2014)
Sala, F., De Sa, C., Gu, A., Ré, C.: Representation tradeoffs for hyperbolic embeddings. In: International Conference on Machine Learning, pp. 4460–4469. PMLR (2018)
Sankar, A., Wu, Y., Gou, L., Zhang, W., Yang, H.: Dysat: deep neural representation learning on dynamic graphs via self-attention networks. In: Proceedings of the 13th International Conference on Web Search and Data Mining, pp. 519–527 (2020)
Shi, C., Li, Y., Zhang, J., Sun, Y., Philip, S.Y.: A survey of heterogeneous information network analysis. IEEE Trans. Knowl. Data Eng. 29(1), 17–37 (2016)
Tang, J., Qu, M., Mei, Q.: Pte: Predictive text embedding through large-scale heterogeneous text networks. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1165–1174 (2015)
Tang, J., Qu, M., Wang, M., Zhang, M., Yan, J., Mei, Q.: Line: large-scale information network embedding. In: Proceedings of the 24th International Conference on World Wide Web, pp. 1067–1077 (2015)
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., Bengio, Y.: Graph attention networks. In: International Conference on Learning Representations (2018)
Wang, X., et al.: Heterogeneous graph attention network. In: The World Wide Web Conference. pp. 2022–2032 (2019)
Wang, X., Zhang, Y., Shi, C.: Hyperbolic heterogeneous information network embedding. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 5337–5344 (2019)
Xu, D., Ruan, C., Körpeoglu, E., Kumar, S., Achan, K.: Inductive representation learning on temporal graphs. In: International Conference on Learning Representations (2020)
Yang, D., Zhang, D., Zheng, V.W., Yu, Z.: Modeling user activity preference by leveraging user spatial temporal characteristics in lbsns. IEEE Trans. Syst. Man Cybern. Syst. 45(1), 129–142 (2014)
Yin, Y., Ji, L.X., Zhang, J.P., Pei, Y.L.: Dhne: network representation learning method for dynamic heterogeneous networks. IEEE Access 7, 134782–134792 (2019)
Zhang, S., Chen, H., Ming, X., Cui, L., Yin, H., Xu, G.: Where are we in embedding spaces? In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pp. 2223–2231 (2021)
Zhang, Y., Wang, X., Shi, C., Jiang, X., Ye, Y.F.: Hyperbolic graph attention network. IEEE Trans. Big Data (2021)
Zheng, Y., Shi, C., Cao, X., Li, X., Wu, B.: Entity set expansion with meta path in knowledge graph. In: Kim, J., Shim, K., Cao, L., Lee, J.-G., Lin, X., Moon, Y.-S. (eds.) PAKDD 2017. LNCS (LNAI), vol. 10234, pp. 317–329. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-57454-7_25
Zhou, L., Yang, Y., Ren, X., Wu, F., Zhuang, Y.: Dynamic network embedding by modeling triadic closure process. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018)
Acknowledgements
This work is supported by the Chinese Scientific and Technical Innovation Project 2030 (2018AAA0102100), National Natural Science Foundation of China (U1936206, U1903128, 62172237) and the Fundamental Research Funds for the Central Universities (No. 63223046).
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
Bai, Q., Guo, J., Zhang, H., Nie, C., Zhang, L., Yuan, X. (2022). \(\text {H}^2\text {TNE}\): Temporal Heterogeneous Information Network Embedding in Hyperbolic Spaces. In: Sattler, U., et al. The Semantic Web – ISWC 2022. ISWC 2022. Lecture Notes in Computer Science, vol 13489. Springer, Cham. https://doi.org/10.1007/978-3-031-19433-7_11
Download citation
DOI: https://doi.org/10.1007/978-3-031-19433-7_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-19432-0
Online ISBN: 978-3-031-19433-7
eBook Packages: Computer ScienceComputer Science (R0)
