[15] W. Hu, M. Fey, M. Zitnik, Y. Dong, H. Ren, B. Liu, M. Catasta, and J. Leskovec. Open graph benchmark: Datasets for machine learning on graphs. In Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual, 2020.
[16] K. Jha, S. Saha, and H. Singh. Prediction of protein-protein interaction using graph neural networks. Scientific Reports, 12(1):8360, 2022.
[17] G. Karypis and V. Kumar. A fast and high quality multilevel scheme for partitioning irregular graphs. SIAM J. Sci. Comput., 20(1):359-392, 1998.
[18] W. O. Kermack and A. G. McKendrick. A contribution to the mathematical theory of epidemics. Proceedings of the Royal Society of London. Series A, Containing papers of a mathematical and physical character, 115(772):700-721, 1927.
[19] T. N. Kipf and M. Welling. Semi-supervised classification with graph convolutional networks. In Proceedings of the 5th International Conference on Learning Representations, ICLR 2017, Toulouse, France, April 24-26, 2017., OpenReview.net, 2017.
[20] P. Li, Y. Wang, H. Zhao, P. Hong, and H. Liu. On dyadic fairness: Exploring and mitigating bias in graph connections. In Proceedings of the 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3-7, 2021. OpenReview.net, 2021.
[21] Z. Li, Y. Dong, C. Gao, Y. Zhao, D. Li, J. Hao, K. Zhang, Y. Li, and Z. Wang. Breaking filter bubble: A reinforcement learning framework of controllable recommender system. In Proceedings of the ACM Web Conference 2023, WWW 2023, Austin, TX, USA, 30 April 2023 - 4 May 2023, pages 4041-4049. ACM, 2023.
[22] G. Linden, B. Smith, and J. York. Amazon.com recommendations: Item-to-item collaborative filtering. IEEE Internet Comput., 7(1):76-80, 2003.
[23] P. Liu, K. Shivaram, A. Culotta, M. A. Shapiro, and M. Bilgic. The interaction between political typology and filter bubbles in news recommendation algorithms. In Proceedings of the Web Conference 2021, Virtual Event / Ljubljana, Slovenia, April 19-23, 2021, pages 3791-3801. ACM / IW3C2, 2021.
[24] S. Liu, B. C. Grau, I. Horrocks, and E. V. Kostylev. INDIGO: gnn-based inductive knowledge graph completion using pair-wise encoding. In Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, NeurIPS 2021, December 6-14, 2021, virtual, pages 2034-2045, 2021.
[25] F. Lorrain and H. C. White. Structural equivalence of individuals in social networks. The Journal of Mathematical Sociology, 1(1):49-80, 1971.
[26] Z. Luo, J. Lian, H. Huang, H. Jin, and X. Xie. Ada-gnn: Adapting to local patterns for improving graph neural networks. In Proceedings of the 15th ACM International Conference on Web Search and Data Mining, Virtual Event / Tempe, AZ, USA, February 21 - 25, 2022, pages 638-647. ACM, 2022.
[27] K. Mao, J. Zhu, X. Xiao, B. Lu, Z. Wang, and X. He. Ultragcn: Ultra simplification of graph convolutional networks for recommendation. In Proceedings of the 30th ACM International Conference on Information and Knowledge Management, Virtual Event, Queensland, Australia, November 1 - 5, 2021, pages 1253-1262. ACM, 2021.
[28] F. Masrour, T. Wilson, H. Yan, P. Tan, and A. Esfahanian. Bursting the filter bubble: Fairness-aware network link prediction. In Proceedings of the 34th AAAI Conference on Artificial Intelligence, AAAI 2020, New York, NY, USA, February 7-12, 2020, pages 841-848. AAAI Press, 2020.
[29] B. Perozzi, R. Al-Rfou, and S. Skiena. Deepwalk: online learning of social representations. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '14, New York, NY, USA - August 24 - 27, 2014, pages 701-710. ACM, 2014.