|
[1]
|
Jain, N. (2020) Domain-Specific Knowledge Graph Construction for Semantic Analysis. In: Harth, A., et al., Eds., The Semantic Web: ESWC 2020 Satellite Events, Springer, Cham, 250-260. [Google Scholar] [CrossRef]
|
|
[2]
|
Zhang, L., He, Q., Yu, W., et al. (2022) Research on Entity Disambiguation Method and Model Construction Based on Knowledge Graph. 2022 4th International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI), Shanghai, 28-30 October 2022, 174-177. [Google Scholar] [CrossRef]
|
|
[3]
|
Wei, L., Zhao, H. and He, Z. (2022) Designing the Topology of Graph Neural Networks: A Novel Feature Fusion Perspective. Proceedings of the ACM Web Conference 2022, Nanjing, 25-29 April 2022, 1381-1391. [Google Scholar] [CrossRef]
|
|
[4]
|
Omar, R., Mangukiya, O., Kalnis, P., et al. (2023) ChatGPT versus Traditional Question Answering for Knowledge Graphs: Current Status and Future Directions towards Knowledge Graph Chatbots. arXiv: 2302.06466. [Google Scholar] [CrossRef]
|
|
[5]
|
Bordes, A., Usunier, N., Garcia-Duran, A., et al. (2013) Translating Embeddings for Modeling Multi-Relational Data. Advances in Neural Information Processing Systems, 26, 2787-2795.
|
|
[6]
|
He, X., Wen, R., Wu, Y., et al. (2021) Node-Level Membership Inference Attacks against Graph Neural Networks. arXiv: 2102.05429.
|
|
[7]
|
Wu, B., Yang, X., Pan, S., et al. (2022) Model Extraction Attacks on Graph Neural Networks: Taxonomy and Realisation. Proceedings of the 2022 ACM on Asia Conference on Computer and Communications Security, Nagasaki, 30 May-3 June 2022, 337-350. [Google Scholar] [CrossRef]
|
|
[8]
|
Zhang, Z., Liu, Q., Huang, Z., et al. (2021) GraphMi: Extracting Private Graph Data from Graph Neural Networks. Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence (IJCAI-21). Montreal, 19-27 August 2021, 3749-3755. [Google Scholar] [CrossRef]
|
|
[9]
|
Wu, H., Wang, C., Tyshetskiy, Y., et al. (2019) Adversarial Examples on Graph Data: Deep Insights into Attack and Defense. arXiv: 1903.01610. [Google Scholar] [CrossRef]
|
|
[10]
|
Entezari, N., Al-Sayouri, S.A., Darvishzadeh, A., et al. (2020) All You Need Is Low (Rank) Defending against Adversarial Attacks on Graphs. Proceedings of the 13th International Conference on Web Search and Data Mining, Houston, 3-7 February 2020, 169-177. [Google Scholar] [CrossRef]
|
|
[11]
|
Liu, N., Yang, H. and Hu, X. (2018) Adversarial Detection with Model Interpretation. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, London, 19-23 August 2018, 1803-1811. [Google Scholar] [CrossRef]
|
|
[12]
|
Feng, F., He, X., Tang, J., et al. (2019) Graph Adversarial Training: Dynamically Regularizing Based on Graph Structure. IEEE Transactions on Knowledge and Data Engineering, 33, 2493-2504. [Google Scholar] [CrossRef]
|
|
[13]
|
Li, J., Peng, J., Chen, L., et al. (2022) Spectral Adversarial Training for Robust Graph Neural Network. IEEE Transactions on Knowledge and Data Engineering, 35, 9240-9253. [Google Scholar] [CrossRef]
|
|
[14]
|
Zhang, Z., Jia, J., Wang, B., et al. (2021) Backdoor Attacks to Graph Neural Networks. Proceedings of the 26th ACM Symposium on Access Control Models and Technologies, New York, 16-18 June 2021, 15-26. [Google Scholar] [CrossRef]
|