[1] L. A. Adamic and E. Adar. Friends and neighbors on the web. Social networks, 25(3):211-230, 2003.
[2] V. D. Blondel, J.-L. Guillaume, R. Lambiotte, and E. Lefebvre. Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 2008(10):P10008, 2008.
[3] A. Bojchevski, J. Klicpera, B. Perozzi, A. Kapoor, M. Blais, B. Rozemberczki, M. Lukasik, and S. Gunnemann. Scaling graph neural networks with approximate pagerank. In Proceedings of the 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Virtual Event, CA, USA, August 23-27, 2020, pages 2464-2473. ACM, 2020.
[4] A. J. Bose and W. L. Hamilton. Compositional fairness constraints for graph embeddings. In Proceedings of the 36th International Conference on Machine Learning, ICML 2019, 9-15 June 2019, Long Beach, California, USA, volume 97 of Proceedings of Machine Learning Research, pages 715-724. PMLR, 2019.
[5] T. Calinski and J. Harabasz. A dendrite method for cluster analysis. Communications in Statistics-theory and Methods, 3(1):1-27, 1974.
[6] U. Chitra and C. Musco. Analyzing the impact of filter bubbles on social network polarization. In Proceedings of the 13th ACM International Conference on Web Search and Data Mining, Houston, TX, USA, February 3-7, 2020, pages 115-123. ACM, 2020.
[7] M. Coskun and M. Koyutürk. Node similarity-based graph convolution for link prediction in biological networks. Bioinformatics, 37(23):4501-4508, 2021.
[8] E. Dai and S. Wang. Say no to the discrimination: Learning fair graph neural networks with limited sensitive attribute information. In Proceedings of the 14th ACM International Conference on Web Search and Data Mining, Virtual Event, Israel, March 8-12, 2021, pages 680-688. ACM, 2021.
[9] Y. Dong, N. Liu, B. Jalaian, and J. Li. EDITS: modeling and mitigating data bias for graph neural networks. In Proceedings of the ACM Web Conference 2022, Virtual Event, Lyon, France, April 25 - 29, 2022, pages 1259-1269. ACM, 2022.
[10] Y. Ge, S. Zhao, H. Zhou, C. Pei, F. Sun, W. Ou, and Y. Zhang. Understanding echo chambers in e-commerce recommender systems. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2020, Virtual Event, China, July 25-30, 2020, pages 2261-2270. ACM, 2020.
[11] M. S. Granovetter. The strength of weak ties. American Journal of Sociology, 78(6):1360-1380, 1973.
[12] A. Grover and J. Leskovec. node2vec: Scalable feature learning for networks. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, August 13-17, 2016, pages 855-864. ACM, 2016.
[13] W. L. Hamilton, Z. Ying, and J. Leskovec. Inductive representation learning on large graphs. In Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, NeurIPS 2017, December 4-9, 2017, Long Beach, CA, USA, pages 1024-1034, 2017.
[14] X. He, K. Deng, X. Wang, Y. Li, Y. Zhang, and M. Wang. Lightgcn: Simplifying and powering graph convolution network for recommendation. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2020, Virtual Event, China, July 25-30, 2020, pages 639-648. ACM, 2020.