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Graph neural networks (GNNs) have gained significant attraction due to their expansive real-world applications.
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2021
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B. Wang, J. Guo, A. Li, Y. Chen, and H. Li, “Privacy-preserving representation learning on graphs: A mutual information perspective,” in KDD . ACM, 2021, pp. 1667–1676
2021
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2021
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2021
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X. Zheng, M. Zhang, C. Chen, C. Li, C. Zhou, and S. Pan, “Multi-relational graph neural architecture search with fine-grained message passing,” in ICDM . IEEE, 2022, pp. 783–792
2022
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B. Wu, X. Yang, S. Pan, and X. Yuan, “Model extraction attacks on graph neural networks: Taxonomy and realization,” in AsiaCCS . ACM, 2022
2022
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L. Gondara, K. Wang, and R. S. Carvalho, “Differentially private ensemble classifiers for data streams,” in WSDM . ACM, 2022, pp. 325–333
2022
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2023
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X. Zheng, M. Zhang, C. Chen, Q. V. H. Nguyen, X. Zhu, and S. Pan, “Structure-free graph condensation: From large-scale graphs to condensed graph-free data,” in NeurIPS , 2023
2023
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2023
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Z. Yin, Q. Zhang, W. Zhang, R. Li, and G. Wang, “Fairness-aware maximal biclique enumeration on bipartite graphs,” in ICDE . IEEE, 2023, pp. 1665–1677
2023
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D. Jin, L. Wang, H. Zhang, Y. Zheng, W. Ding, F. Xia, and S. Pan, “A survey on fairness-aware recommender systems,” Inf. Fusion , vol. 100, p. 101906, 2023
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2023
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X. Zheng, M. Zhang, C. Chen, S. Molaei, C. Zhou, and S. Pan, “Gnnevaluator: Evaluating gnn performance on unseen graphs without labels,” in NeurIPS , 2023
2023
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2023
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Y. Liu, K. Ding, Q. Lu, F. Li, L. Y. Zhang, and S. Pan, “Towards self-interpretable graph-level anomaly detection,” in NeurIPS , 2023
2023
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X. Wang, T. Gu, X. Bao, L. Chang, and L. Li, “Individual fairness for local private graph neural network,” Knowl. Based Syst. , vol. 268, p. 110490, 2023
2023
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2023
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H. Zhang, B. Wu, S. Wang, X. Yang, M. Xue, S. Pan, and X. Yuan, “Demystifying uneven vulnerability of link stealing attacks against graph neural networks,” in ICML , ser. Proceedings of Machine Learning Research, vol. 202. PMLR, 2023, pp. 41 737–41 752
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Y. Gao, P. Wang, X. Zeng, L. Chen, Y. Mao, Z. Wei, and M. Li, “Towards explainable table interpretation using multi-view explanations,” in ICDE . IEEE, 2023, pp. 1167–1179
2023
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J. Rorseth, P. Godfrey, L. Golab, M. Kargar, D. Srivastava, and J. Szlichta, “CREDENCE: counterfactual explanations for document ranking,” in ICDE . IEEE, 2023, pp. 3631–3634
2023
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L. Wang, D. He, H. Zhang, Y. Liu, W. Wang, S. Pan, D. Jin, and T. Chua, “GOODAT: towards test-time graph out-of-distribution detection,” AAAI , 2024
2024
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S. Pan, L. Luo, Y. Wang, C. Chen, J. Wang, and X. Wu, “Unifying large language models and knowledge graphs: A roadmap,” IEEE TKDE , 2024
2024
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S. Pan, Y. Zheng, and Y. Liu, “Integrating graphs with large language models: Methods and prospects,” IEEE Intelligent Systems , vol. 39, no. 1, pp. 64–68, 2024
2024
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M. Jin, S. Wang, L. Ma, Z. Chu, J. Y. Zhang, X. Shi, P. Chen, Y. Liang, Y. Li, S. Pan, and Q. Wen, “Time-llm: Time series forecasting by reprogramming large language models,” ICLR , 2024
2024
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B. Wu, H. Zhang, X. Yang, S. Wang, M. Xue, S. Pan, and X. Yuan, “Graphguard: Detecting and counteracting training data misuse in graph neural networks,” NDSS , 2024
2024
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H. Zhang, B. Wu, X. Yuan, S. Pan, H. Tong, and J. Pei, “Trustworthy graph neural networks: Aspects, methods and trends,” Proceedings of the IEEE , 2024
2024
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L. Luo, Y.-F. Li, G. Haffari, and S. Pan, “Reasoning on graphs: Faithful and interpretable large language model reasoning,” in ICLR , 2024
2024
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F. Wu, Y. Long, C. Zhang, and B. Li, “LINKTELLER: recovering private edges from graph neural networks via influence analysis,” in IEEE Symposium on Security and Privacy . IEEE, 2022, pp. 2005–2024
2024
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