[42] Shirui Pan, Ruiqi Hu, Guodong Long, Jing Jiang, Lina Yao, and Chengqi Zhang. 2018. Adversarially regularized graph autoencoder for graph embedding. In Proceedings of International Joint Conference on Artificial Intelligence (IJCAI). 2609-2615.
[43] Shirui Pan, Linhao Luo, Yufei Wang, Chen Chen, Jiapu Wang, and Xindong Wu. 2023. Unifying Large Language Models and Knowledge Graphs: A Roadmap. arXiv preprint arxiv:2306.08302 (2023).
[44] Shirui Pan, Yizhen Zheng, and Yixin Liu. 2023. Integrating Graphs with Large Language Models: Methods and Prospects. arXiv preprint arXiv:2310.05499 (2023).
[45] German I Parisi, Ronald Kemker, Jose L Part, Christopher Kanan, and Stefan Wermter. 2019. Continual lifelong learning with neural networks: A review. *Neural networks* 113 (2019), 54-71.
[46] Yijian Qin, Xin Wang, Zeyang Zhang, and Wenwu Zhu. 2021. Graph differentiable architecture search with structure learning. In Advances in Neural Information Processing Systems (NeurIPS), Vol. 34. 16860-16872.
[47] Sylvestre-Alvise Rebuffi, Alexander Kolesnikov, Georg Sperl, and Christoph H Lampert. 2017. iCaRL: Incremental Classifier and Representation Learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 2001-2010.
[48] Andrea Rosasco, Antonio Carta, Andrea Cossu, Vincenzo Lomonaco, and Davide Bacciu. 2022. Distilled replay: Overcoming forgetting through synthetic samples. In Continual Semi-Supervised Learning: First International Workshop (CSSL). Springer, 104-117.
[49] Noveen Sachdeva and Julian McAuley. 2023. Data Distillation: A Survey. arXiv preprint arXiv:2301.04272 (2023).
[50] Mattia Sangermano, Antonio Carta, Andrea Cossu, and Davide Bacciu. 2022. Sample condensation in online continual learning. In International Joint Conference on Neural Networks (IJCNN). IEEE, 01-08.
[51] Ozan Sener and Silvio Savarese. 2018. Active Learning for Convolutional Neural Networks: A Core-Set Approach. In International Conference on Learning Representations (ICLR).
[52] Junyuan Shang, Tengfei Ma, Cao Xiao, and Jimeng Sun. 2019. Pre-training of graph augmented transformers for medication recommendation. In International Joint Conference on Artificial Intelligence (IJCAI). 5953-5959.
[53] Yiwei Sun, Suhang Wang, Xianfeng Tang, Tsung-Yu Hsieh, and Vasant Honavar. 2020. Adversarial attacks on graph neural networks via node injections: A hierarchical reinforcement learning approach. In Proceedings of the Web Conference (WWW). 673-683.
[54] Yue Tan, Yixin Liu, Guodong Long, Jing Jiang, Qinghua Lu, and Chengqi Zhang. 2023. Federated learning on non-iid graphs via structural knowledge sharing. In Proceedings of the AAAI conference on artificial intelligence, Vol. 37. 9953-9961.
[55] Laurens Van der Maaten and Geoffrey Hinton. 2008. Visualizing Data Using t-SNE. Journal of Machine Learning Research 9, 11 (2008).
[56] Petar Velicković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2018. Graph Attention Networks. In International Conference on Learning Representations (ICLR).
[57] C Wang, S Pan, R Hu, G Long, J Jiang, and C Zhang. 2019. Attributed Graph Clustering: A Deep Attentional Embedding Approach. In Proceedings of International Joint Conference on Artificial Intelligence (IJCAI).
[58] Chun Wang, Shirui Pan, Guodong Long, Xingquan Zhu, and Jing Jiang. 2017. Mgae: Marginalized graph autoencoder for graph clustering. In Proceedings of ACM on Conference on Information and Knowledge Management (CIKM). 889-898.