THS-GWNN: a deep learning framework for temporal network link prediction

References

[1] Li B , Pi D . Network representation learning: a systematic literature review. Neural Computing and Applications, 2020, 32( 7): 1433– 3058

[2] Xu B, Shen H, Cao Q, Qiu Y, Cheng X. Graph wavelet neural network. In: Proceedings of the 7th International Conference on Learning Representations. 2019, 1−13

[3] Chen H, Li J. Exploiting structural and temporal evolution in dynamic link prediction. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management. 2018, 427−436

[4] Zhao L , Song Y , Zhang C , Liu Y , Wang P . T-GCN: a temporal graph convolutional network for traffic prediction. IEEE Transactions on Intelligent Transportation Systems, 2020, 21( 9): 3848– 3858

[5] Yu W, Cheng W, Aggarwal C C, Zhang K, Chen H, Wang W. NetWalk: a flexible deep embedding approach for anomaly detection in dynamic networks. In: Proceedings of the 24th ACM Conference on Knowledge Discovery and Data Mining. 2018, 2672−2681

[6] Hochreiter S , Schmidhuber J . Long short-term memory. Neural Computation, 1987, 9( 8): 1735– 1780

[7] Ying R, He R, Chen K, Eksombatchai P, Hamilton W L. Graph convolutional neural networks for web-scale recommender Systems. In: Proceedings of the 24th ACM Conference on Knowledge Discovery and Data Mining. 2018, 974−983

Acknowledgements

This work has been supported by Chongqing Graduate Student Research and Innovation Project (CYB19096), the China Scholarship Council (202006990041), the Fundamental Research Funds for the Central Universities (XDJK2020D021), the Capacity Development Grant of Southwest University (SWU116007), and the National Natural Science Foundation of China (Grant Nos. 61672435, 61732019, 61811530327)

Supplementary materials

The supporting information is available online at journal.hep.com.cn and link.springer.com.

RIGHTS & PERMISSIONS

2022 Higher Education Press