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何玉林 等: 基于时空注意力的多粒度链路预测算法 4325
References:
[1] Zhu HR, Xiong F, Chen HS, Xiong X, Wang L. Incorporating a triple graph neural network with multiple implicit feedback for social
recommendation. ACM Trans. on the Web, 2024, 18(2): 23. [doi: 10.1145/3580517]
[2] Nunes I, Heddes M, Vergés P, Abraham D, Veidenbaum A, Nicolau A, Givargis T. DotHash: Estimating set similarity metrics for link
prediction and document deduplication. In: Proc. of the 29th ACM SIGKDD Int’l Conf. on Knowledge Discovery and Data Mining. Long
Beach: ACM, 2023. 1758–1769. [doi: 10.1145/3580305.3599314]
[3] Ma ZM, Nandy S. Community detection with contextual multilayer networks. IEEE Trans. on Information Theory, 2023, 69(5):
3203–3239. [doi: 10.1109/TIT.2023.3238352]
[4] Billah MM, Sultana A, Bhuiyan F, Kaosar MG. Stock price prediction: Comparison of different moving average techniques using deep
learning model. Neural Computing and Applications, 2024, 36(11): 5861–5871. [doi: 10.1007/s00521-023-09369-0]
[5] Sharma A, Yadav AK, Rai AK. A novel and precise approach for similarity-based link prediction in diverse networks. Social Network
Analysis and Mining, 2023, 14(1): 11. [doi: 10.1007/s13278-023-01160-2]
[6] Jiang SY, Xu XK, Xiao J. Link prediction by combining local structure similarity with node behavior synchronization. IEEE Trans. on
Computational Social Systems, 2023, 11(3): 3816–3825. [doi: 10.1109/TCSS.2023.3335295]
[7] Charikhi M. Association of the PageRank algorithm with similarity-based methods for link prediction in complex networks. Physica A:
Statistical Mechanics and Its Applications, 2024, 637: 129552. [doi: 10.1016/j.physa.2024.129552]
[8] Kumar S, Mallik A, Panda BS. Link prediction in complex networks using node centrality and light gradient boosting machine. World
Wide Web, 2022, 25(6): 2487–2513. [doi: 10.1007/s11280-021-01000-3]
[9] Chaubey VP, Sharma A, Sharma T, Sharma S, Kumar A. Link prediction for social network analysis using random forest and XG-boost
algorithm. In: Proc. of the 2nd Int’l Conf. on Informatics. Noida: IEEE, 2023. 1–6. [doi: 10.1109/ICI60088.2023.10421655]
[10] Choudhury N. Community-aware evolution similarity for link prediction in dynamic social networks. Mathematics, 2024, 12(2): 285.
[doi: 10.3390/math12020285]
[11] Ma XK, Tan SY, Xie XH, Zhong XX, Deng JJ. Joint multi-label learning and feature extraction for temporal link prediction. Pattern
Recognition, 2022, 121: 108216. [doi: 10.1016/j.patcog.2021.108216]
[12] Lv LS, Bardou D, Liu YQ, Hu P. Deep Autoencoder-like non-negative matrix factorization with graph regularized for link prediction in
dynamic networks. Applied Soft Computing, 2023, 148: 110832. [doi: 10.1016/j.asoc.2023.110832]
[13] Yan LP, Yu WR. Non-negative matrix factorization for link prediction preserving row and column spaces. In: Proc. of the 2023 IEEE Int’l
Conf. on Data Mining. Shanghai: IEEE, 2023. 1451–1456. [doi: 10.1109/ICDM58522.2023.00190]
[14] Lin XY, Chen XY, Zheng ZW. Deep manifold matrix factorization autoencoder using global connectivity for link prediction. Applied
Intelligence, 2023, 53(21): 25816–25835. [doi: 10.1007/s10489-023-04887-9]
[15] Mahmoodi R, Seyedi SA, Tab FA, Abdollahpouri A. Link prediction by adversarial nonnegative matrix factorization. Knowledge-based
Systems, 2023, 280: 110998. [doi: 10.1016/j.knosys.2023.110998]
[16] Nasiri E, Berahmand K, Li YF. Robust graph regularization nonnegative matrix factorization for link prediction in attributed networks.
Multimedia Tools and Applications, 2023, 82(3): 3745–3768. [doi: 10.1007/s11042-022-12943-8]
[17] Chen GF, Wang HB, Fang YL, Jiang L. Link prediction by deep non-negative matrix factorization. Expert Systems with Applications,
2022, 188: 115991. [doi: 10.1016/j.eswa.2021.115991]
[18] Yao YB, He YY, Huang ZT, Xu ZP, Yang F, Tang JX, Gao K. Deep non-negative matrix factorization with edge generator for link
prediction in complex networks. Applied Intelligence, 2024, 54(1): 592–613. [doi: 10.1007/s10489-023-05211-1]
[19] Min SJ, Gao Z, Peng J, Wang L, Qin K, Fang B. STGSN—A spatial-temporal graph neural network framework for time-evolving social
networks. Knowledge-based Systems, 2021, 214: 106746. [doi: 10.1016/j.knosys.2021.106746]
[20] Jiao PF, Guo X, Jing X, He DX, Wu HM, Pan SR, Gong MG, Wang WJ. Temporal network embedding for link prediction via VAE joint
attention mechanism. IEEE Trans. on Neural Networks and Learning Systems, 2022, 33(12): 7400–7413. [doi: 10.1109/TNNLS.2021.
3084957]
[21] Chen JY, Wang XK, Xu XH. GC-LSTM: Graph convolution embedded LSTM for dynamic network link prediction. Applied Intelligence,
2022, 52(7): 7513–7528. [doi: 10.1007/s10489-021-02518-9]
[22] Tan SY, You JY, Li DY. Temporality- and frequency-aware graph contrastive learning for temporal network. In: Proc. of the 31st ACM
Int’l Conf. on Information & Knowledge Management. Atlanta: ACM, 2022. 1878–1888. [doi: 10.1145/3511808.3557469]
[23] Liu LF, Yu ZX, Zhu H. A link prediction method based on gated recurrent units for mobile social network. Journal of Computer Research
and Development, 2023, 60(3): 705–716 (in Chinese with English abstract). [doi: 10.7544/issn1000-1239.202110432]
[24] He P, Wei C, Lü SK, Zeng C, Li B. GoGCN for interaction prediction between classes in software system. Ruan Jian Xue Bao/Journal of
Software, 2023, 34(11): 5029–5041 (in Chinese with English abstract). http://www.jos.org.cn/1000-9825/6678.htm [doi: 10.13328/j.cnki.
jos.006678]

