References(47)
[1]
X. Zheng and Z. Cai, Privacy-preserved data sharing towards multiple parties in industrial IoTs, IEEE J. Sel. Areas Commun., vol. 38, no. 5, pp. 968–979, 2020.
[2]
Z. Cai and X. Zheng, A private and efficient mechanism for data uploading in smart cyber-physical systems, IEEE Trans. Netw. Sci. Eng., vol. 7, no. 2, pp. 766–775, 2020.
[3]
Z. Cai, X. Zheng, J. Wang, and Z. He, Private data trading towards range counting queries in Internet of Things, IEEE Trans. Mob. Comput., vol. 22, no. 8, pp. 4881–4897, 2023.
[4]
Z. Cai, Z. He, X. Guan, and Y. Li, Collective data-sanitization for preventing sensitive information inference attacks in social networks, IEEE Trans. Dependable Secure Comput., vol. 15, no. 4, pp. 577–590, 2018.
[5]
Y. Liang, Z. Cai, J. Yu, Q. Han, and Y. Li, Deep learning based inference of private information using embedded sensors in smart devices, IEEE Netw., vol. 32, no. 4, pp. 8–14, 2018.
[6]
Y. Huang, Y. J. Li, and Z. Cai, Security and privacy in metaverse: A comprehensive survey, Big Data Mining and Analytics, vol. 6, no. 2, pp. 234–247, 2023.
[7]
I. K. Nti, J. A. Quarcoo, J. Aning, and G. K. Fosu, A mini-review of machine learning in big data analytics: Applications, challenges, and prospects, Big Data Mining and Analytics, vol. 5, no. 2, pp. 81–97, 2022.
[8]
Z. Wu, S. Pan, F. Chen, G. Long, C. Zhang, and P. S. Yu, A comprehensive survey on graph neural networks, IEEE Trans. Neural Netw. Learn. Syst., vol. 32, no. 1, pp. 4–24, 2021.
[9]
B. M. Oloulade, J. Gao, J. Chen, T. Lyu, and R. Al-Sabri, Graph neural architecture search: A survey, Tsinghua Science and Technology, vol. 27, no. 4, pp. 692–708, 2022.
[10]
M. Zhang, Z. Cui, M. Neumann, and Y. Chen, An end-to- end deep learning architecture for graph classification, Proc. AAAI Conf. Artif. Intell., vol. 32, no. 1, pp. 4438–4445, 2018.
[11]
Y. Duan, J. Wang, H. Ma, and Y. Sun, Residual convolutional graph neural network with subgraph attention pooling, Tsinghua Science and Technology, vol. 27, no. 4, pp. 653–663, 2022.
[12]
F. Errica, M. Podda, D. Bacciu, and A. Micheli, A fair comparison of graph neural networks for graph classification, arXiv preprint arXiv: 1912.09893, 2019.
[13]
H. Zhao, H. Chen, L. Li, and H. Wan, Understanding social relationships with person-pair relations, Big Data Mining and Analytics, vol. 5, no. 2, pp. 120–129, 2022.
[14]
C. Eksombatchai, P. Jindal, J. Z. Liu, Y. Liu, R. Sharma, C. Sugnet, M. Ulrich, and J. Leskovec, Pixie: A system for recommending 3+ billion items to 200+ million users in real-time, in Proc. 2018 World Wide Web Conference, 2018, pp. 1775–1784.
[15]
Y. Wang, Q. Yao, J. T. Kwok, and L. M. Ni, Generalizing from a few examples: A survey on few-shot learning, ACM Comput. Surv., vol. 53, no. 3, p. 63, 2020.
[16]
Y. Chen, Z. Liu, H. Xu, T. Darrell, and X. Wang, Meta-baseline: Exploring simple meta-learning for few-shot learning, in Proc. 2021 IEEE/CVF Int. Conf. Computer Vision (ICCV), Montreal, Canada, 2021, pp. 9042–9051.
[17]
T. Hospedales, A. Antoniou, P. Micaelli, and A. Storkey, Meta-learning in neural networks: A survey, IEEE Trans. Pattern Anal. Mach. Intell., vol. 44, no. 9, pp. 5149–5169, 2021.
[18]
O. Vinyals, C. Blundell, T. Lillicrap, K. Kavukcuoglu, and D. Wierstra, Matching networks for one shot learning, in Proc. 30th Int. Conf. Neural Information Processing Systems, Barcelona, Spain, 2016, pp. 3637–3645.
[19]
J. Snell, K. Swersky, and R. Zemel, Prototypical networks for few-shot learning, in Proc. 31st Int. Conf. Neural Information Processing Systems, 2017, pp. 4080–4090.
[20]
C. Finn, P. Abbeel, and S. Levine, Model-agnostic meta-learning for fast adaptation of deep networks, in Proc. 34th Int. Conf. Machine Learning - Volume 70, Sydney, Australia, 2017, pp. 1126–1135.
[21]
N. Ma, J. Bu, J. Yang, Z. Zhang, C. Yao, Z. Yu, S. Zhou, and X. Yan, Adaptivestep graph meta-learner for few-shot graph classification, in Proc. 29th ACM Int. Conf. Information & Knowledge Management, Virtual Event, 2020, pp. 1055–1064.
[22]
J. Chauhan, D. Nathani, and M. Kaul, Few-shot learning on graphs via super-classes based on graph spectral measures, arXiv preprint arXiv: 2002.12815, 2020.
[23]
S. Jiang, F. Feng, W. Chen, X. Li, and X. He, Structure-enhanced meta-learning for few-shot graph classification, AI Open, vol. 2, pp. 160–167, 2021.
[24]
K. Xu, W. Hu, J. Leskovec, and S. Jegelka, How powerful are graph neural networks? arXiv preprint arXiv: 1810.00826v1, 2019.
[25]
K. Hassani, Cross-domain few-shot graph classification, arXiv preprint arXiv: 2201.08265, 2022.
[26]
T. Chen, S. Kornblith, M. Norouzi, and G. Hinton, A simple framework for contrastive learning of visual representations, in Proc. 37th Int. Conf. Machine Learning, Vienna, Austria, 2020, pp. 1597–1607.
[27]
Y. You, T. Chen, Y. Sui, T. Chen, Z. Wang, and Y. Shen, Graph contrastive learning with augmentations, in Proc. 34th Int. Conf. Neural Information Processing Systems, Vancouver, Canada, 2020, pp. 5812–5823.
[28]
Y. You, T. Chen, Y. Shen, and Z. Wang, Graph contrastive learning automated, arXiv preprint arXiv: 2106.07594, 2021.
[29]
Y. Zhu, Y. Xu, F. Yu, Q. Liu, S. Wu, and L. Wang, Graph contrastive learning with adaptive augmentation, in Proc. Web Conf. 2021, Ljubljana, Slovenia, 2021, pp. 2069–2080.
[30]
Y. Zhu, Y. Xu, F. Yu, Q. Liu, S. Wu, and L. Wang, Deep graph contrastive representation learning, arXiv preprint arXiv: 2006.04131, 2020.
[31]
D. Xu, W. Cheng, D. Luo, H. Chen, and X. Zhang, InfoGCL: Information-aware graph contrastive learning, arXiv preprint arXiv: 2110.15438, 2021.
[32]
S. Suresh, P. Li, C. Hao, and J. Neville, Adversarial graph augmentation to improve graph contrastive learning, arXiv preprint arXiv: 2106.05819v4, 2021.
[33]
S. Lin, C. Liu, P. Zhou, Z. Y. Hu, S. Wang, R. Zhao, Y. Zheng, L. Lin, E. Xing, and X. Liang, Prototypical graph contrastive learning, IEEE Trans. Neural Netw. Learn. Syst., .
[34]
J. Yu, H. Yin, X. Xia, T. Chen, L. Cui, and Q. V. H. Nguyen, Are graph augmentations necessary? Simple graph contrastive learning for recommendation, in Proc. 45th Int. ACM SIGIR Conf. Research and Development in Information Retrieval, Madrid, Spain, 2022, pp. 1294–1303.
[35]
Y. Zhu, Y. Xu, Q. Liu, and S. Wu, An empirical study of graph contrastive learning, arXiv preprint arXiv: 2109.01116v2, 2021.
[36]
S. Kolouri, N. Naderializadeh, G. K. Rohde, and H. Hoffmann, Wasserstein embedding for graph learning, arXiv preprint arXiv: 2006.09430, 2020.
[37]
K. He, H. Fan, Y. Wu, S. Xie, and R. Girshick, Momentum contrast for unsupervised visual representation learning, in Proc. 2020 IEEE/CVF Conf. Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA, 2020, pp. 9726–9735.
[39]
J. McAuley, R. Pandey, and J. Leskovec, Inferring networks of substitutable and complementary products, in Proc. 21th ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, Sydney, Australia, 2015, pp. 785–794.
[40]
J. Tang, J. Zhang, L. Yao, J. Li, L. Zhang, and Z. Su, ArnetMiner: Extraction and mining of academic social networks, in Proc. 14th ACM SIGKDD Int. Conf. Knowledge discovery and data mining, Las Vegas, NV, USA, 2008, pp. 990–998.
[41]
N. Wang, M. Luo, K. Ding, L. Zhang, J. Li, and Q. Zheng, Graph few-shot learning with attribute matching, in Proc. 29th ACM Int. Conf. Information & Knowledge Management, Virtual Event, 2020, pp. 1545–1554.
[43]
N. Shervashidze, P. Schweitzer, E. J. Van Leeuwen, K. Mehlhorn, and K. M. Borgwardt, Weisfeiler-lehman graph kernels, J. Mach. Learn. Res., vol. 12, pp. 2539–2561, 2011.
[44]
D. Xu, C. Ruan, E. Korpeoglu, S. Kumar, and K. Achan, Inductive representation learning on temporal graphs, arXiv preprint arXiv: 2002.07962v1, 2020.
[45]
T. N. Kipf and M. Welling, Semi-supervised classification with graph convolutional networks, arXiv preprint arXiv: 1609.02907, 2016.
[46]
P. Velikovi, G. Cucurull, A. Casanova, A. Romero, P. Liò, and Y. Bengio, Graph attention networks, arXiv preprint arXiv: 1710.10903v3, 2018.
[47]
F. M. Bianchi, D. Grattarola, and C. Alippi, Mincut pooling in graph neural networks, arXiv preprint arXiv: 1907.00481v2, 2020.