Abstract
Capturing dynamic changes of networks can greatly improve the representation ability of nodes, which leads to dynamic network embedding becoming a hot research topic nowadays. However, current work focus on the correlation information and the position information of nodes, while the valuable timestamp information of edges is ignored. The timestamp information of edges presents the revolution of dynamic networks, which is extremely important for the dynamic node influence evaluation. To solve the problems of the existing works, we propose a novel dynamic network embedding method with multiple sequences learnings (DEMS). DEMS uses node sequence learning and edge sequence learning simultaneously to preserve more information of node dynamics in the network embedding. Specifically, node sequence learning preserves the node position information, and edge sequence learning preserves the edge timestamp information. Self-Attention mechanism is used in both sequence learnings to preserve the correlation information. Experiments on seven real-world dynamic networks verify the superiority of DEMS to the state-of-the-art methods in temporal link prediction tasks.
Similar content being viewed by others
References
Battaglia PW, Hamrick JB, Bapst V, Sanchez-Gonzalez A, Zambaldi V, Malinowski M, Tacchetti A, Raposo D, Santoro, A, Faulkner R et al (2018) Relational inductive biases, deep learning, and graph networks. arXiv preprint arXiv:1806.01261
Cao S, Lu W, Xu, Q.: Grarep, (2015) Grarep: learning graph representations with global structural information. In: Proceedings of the 24th ACM international on conference on information and knowledge management, pp 891–900
Cao S, Lu W, Xu Q (2016) Deep neural networks for learning graph representations. In: Thirtieth AAAI conference on artificial intelligence
Chen J, Li K, Li K, Yu PS, Zeng Z (2021) Dynamic planning of bicycle stations in dockless public bicycle-sharing system using gated graph neural network. ACM Trans Intell Syst Technol (TIST) 12(2):1–22
Du L, Wang Y, Song G, Lu Z, Wang J (2018) Dynamic network embedding: an extended approach for skip-gram based network embedding. In: IJCAI, pp 2086–2092
Gehring J, Auli M, Grangier D, Yarats D, Dauphin YN (2017) Convolutional sequence to sequence learning. arXiv preprint arXiv:1705.03122
Goyal P, Ferrara E (2018) Graph embedding techniques, applications, and performance: a survey. Knowl Based Syst 151:78–94
Grover A, Leskovec J (2016) node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining, pp 855–864
Hajiramezanali E, Hasanzadeh A, Narayanan K, Duffield N, Zhou M, Qian X (2019) Variational graph recurrent neural networks. In: Advances in neural information processing systems, pp. 10701–10711
Huang X, Li J, Hu, X.:SIAM, (2017) Accelerated attributed network embedding. In: Proceedings of the 2017 SIAM international conference on data mining. SIAM, pp. 633–641
Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980
Kipf TN, Welling M (2016) Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907
Kumar S, Zhang X, Leskovec J (2019) Predicting dynamic embedding trajectory in temporal interaction networks. In: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining, pp 1269–1278
Li J, Dani H, Hu X, Tang J, Chang Y, Liu H (2017) Attributed network embedding for learning in a dynamic environment. In: Proceedings of the 2017 ACM on conference on information and knowledge management, pp 387–396
Mei H, Eisner JM (2017) The neural Hawkes process: a neurally self-modulating multivariate point process. In: Advances in neural information processing systems, pp 6754–6764
Mikolov, T, Chen K, Corrado G, Dean, J (2013) Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781
Nguyen GH, Lee JB, Rossi RA, Ahmed NK, Koh E, Kim S (2018) Continuous-time dynamic network embeddings. In: Companion proceedings of the the web conference 2018, pp 969–976
Perozzi B, Al-Rfou R, Skiena, S., Deepwalk (2014) Deepwalk: online learning of social representations. In: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, pp 701–710
Qiu Z, Hu W, Wu J, Liu W, Du B, Jia X (2020) Temporal network embedding with high-order nonlinear information. In: AAAI, pp 5436–5443
Sak H, Senior A, Beaufays F (2014) Long short-term memory based recurrent neural network architectures for large vocabulary speech recognition. arXiv preprint arXiv:1402.1128
Sutskever I, Vinyals O, Le QV (2014) Sequence to sequence learning with neural networks. In: Advances in neural information processing systems, pp 3104–3112
Trivedi R, Farajtabar M, Biswal P, Zha H (2018) Dyrep: learning representations over dynamic graphs. In: International conference on learning representations
Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Ł, Polosukhin I (2017) Attention is all you need. In: Advances in neural information processing systems, pp 5998–6008
Wang D, Cui P, Zhu W (2016) Structural deep network embedding. In: Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining, pp 1225–1234
Yu B, Lu B, Zhang C, Li C, Pan K (2020) Node proximity preserved dynamic network embedding via matrix perturbation. Knowl Based Syst
Yu W, Cheng W, Aggarwal CC, Zhang K, Chen H, Wang W (2018) A flexible deep embedding approach for anomaly detection in dynamic networks. In: Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining, pp 2672–2681
Zhang Z, Cui P, Pei J, Wang X, Zhu W (2018) Timers: error-bounded SVD restart on dynamic networks. In: Thirty-second AAAI conference on artificial intelligence
Zhang Z, Yang H, Bu J, Zhou S, Yu P, Zhang J, Ester M, Wang C (2018) Anrl: attributed network representation learning via deep neural networks. In: IJCAI, vol 18, pp 3155–3161
Zhou L, Yang Y, Ren X, Wu F, Zhuang Y (2018) Dynamic network embedding by modeling triadic closure process. In: Proceedings of the AAAI conference on artificial intelligence
Zhou S, Yang H, Wang X, Bu J, Ester M, Yu P, Zhang J, Wang C (2018) Prre: personalized relation ranking embedding for attributed networks. In: Proceedings of the 27th ACM international conference on information and knowledge management, pp 823–832
Zhu L, Guo D, Yin J, Ver Steeg G, Galstyan A (2016) Scalable temporal latent space inference for link prediction in dynamic social networks. IEEE Trans Knowl Data Eng 28(10):2765–2777
Zuo Y, Liu G, Lin H, Guo J, Hu X, Wu J (2018) Embedding temporal network via neighborhood formation. In: Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining, pp 2857–2866
Acknowledgements
This work was supported by the Key Research and Development Program of Jiangsu Province (BE2019012), and Joint Fund of National Natural Science Foundation of China and Civil Aviation Administration of China (U2033202).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Yuan, W., Shi, C. & Guan, D. Dynamic network embedding via multiple sequence learning. Neural Comput & Applic 34, 3843–3855 (2022). https://doi.org/10.1007/s00521-021-06646-8
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00521-021-06646-8