Abstract
Temporal link prediction aims to predict future links by learning the structural information and temporal evolution of a network. However, existing methods are heavily dependent on the latest snapshots, which hinders their power to reveal the essential evolutionary patterns based on and leverage them for dynamical inference. As a result, they generally achieve better predictions for the closest future snapshots than remote ones. Moreover, most methods do not take into account the effects of higher-order and global structure. To address these issues, we propose Structure-Enhanced Graph Neural Ordinary Differential Equation (SEGODE), a framework effectively performing dynamic inference by neural ordinary differential equation incorporating attention mechanisms and empowering to capture higher-order and global structure. To validate the proposed model, we conduct multiple experiments on a total of seven real datasets. The experimental results show that the SEGODE not only achieves good performance in link prediction but also maintains excellent results even under data-scarce conditions.
J. Hou and X. Guo—contribute equally.
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References
Bianconi, G., Darst, R.K., Iacovacci, J., et al.: Triadic closure as a basic generating mechanism of communities in complex networks. Phys. Rev. E 90, 042806 (2014)
Calvo, M., Montijano, J., Randez, L.: A fifth-order interpolant for the Dormand and prince Runge-Kutta method. J. Comput. Appl. Math. 29(1), 91–100 (1990)
Chen, R.T., Rubanova, Y., Bettencourt, J., et al.: Neural ordinary differential equations. In: Advances in Neural Information Processing Systems, vol. 31 (2018)
Gao, J., Ribeiro, B.: On the equivalence between temporal and static equivariant graph representations. In: International Conference on Machine Learning (2022)
Goyal, P., Chhetri, S.R., Canedo, A.: dyngraph2vec: capturing network dynamics using dynamic graph representation learning. Knowl.-Based Syst. 187 (2020)
Goyal, P., Kamra, N., He, X., Liu, Y.: DynGEM: deep embedding method for dynamic graphs. In: IJCAI International Workshop on Representation Learning for Graphs (2018)
Hočevar, T., Demšar, J.: A combinatorial approach to graphlet counting. Bioinformatics 30(4), 559–565 (2014)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: International Conference on Learning Representations (2017)
Kumar, S., Zhang, X., Leskovec, J.: Predicting dynamic embedding trajectory in temporal interaction networks. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (2019)
Ma, X., Tan, S., Xie, X., et al.: Joint multi-label learning and feature extraction for temporal link prediction. Pattern Recognit. 121 (2022)
Min, S., Gao, Z., Peng, J., et al.: STGSN–a spatial-temporal graph neural network framework for time-evolving social networks. Knowl.-Based Syst. (2021)
Murphy, C., Laurence, E., Allard, A.: Deep learning of contagion dynamics on complex networks. Nat. Commun. 12(1), 4720 (2021)
Page, L., Brin, S., Motwani, R., Winograd, T.: The PageRank citation ranking: bringing order to the web. Technical report, Stanford InfoLab (1999)
Pareja, A., Domeniconi, G., Chen, J., et al.: EvolveGCN: evolving graph convolutional networks for dynamic graphs. In: Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence (2020)
Pontryagin, L.S.: Mathematical Theory of Optimal Processes. CRC Press, Boca Raton (1987)
Sankar, A., Wu, Y., Gou, L., et al.: 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 (2020)
Tang, J., Zhang, J., Yao, L., et al.: ArnetMiner: extraction and mining of academic social networks. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2008)
You, J., Du, T., Leskovec, J.: ROLAND: graph learning framework for dynamic graphs. In: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (2022)
Yu, W., Cheng, W., Aggarwal, C.C., et al.: Link prediction with spatial and temporal consistency in dynamic networks. In: International Joint Conference on Artificial Intelligence (2017)
Zang, C., Wang, F.: Neural dynamics on complex networks. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (2020)
Acknowledgment
In this work, we were supported by the Shenzhen Sustainable Development Project (KCXFZ20211020172544004) and the Natural Science Foundation of Inner Mongolia Autonomous Region of China (2022LHMS06008).
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Hou, J., Guo, X., Liu, J., Li, J., Pan, L., Wang, W. (2023). Structure-Enhanced Graph Neural ODE Network for Temporal Link Prediction. In: Iliadis, L., Papaleonidas, A., Angelov, P., Jayne, C. (eds) Artificial Neural Networks and Machine Learning – ICANN 2023. ICANN 2023. Lecture Notes in Computer Science, vol 14257. Springer, Cham. https://doi.org/10.1007/978-3-031-44216-2_46
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