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

RIGHTS & PERMISSIONS

Higher Education Press