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From Canonical Correlation Analysis to Self-supervised Graph Neural Networks. In NeurIPS 2021. 76--89."},{"key":"e_1_3_2_1_97_1","doi-asserted-by":"crossref","unstructured":"Xingyi Zhang Zixuan Weng and Sibo Wang. 2024. Towards Deeper Understanding of PPR-based Embedding Approaches: A Topological Perspective. In WWW. 969--979.","DOI":"10.1145\/3589334.3645663"},{"key":"e_1_3_2_1_98_1","doi-asserted-by":"crossref","unstructured":"Xingyi Zhang Kun Xie Sibo Wang and Zengfeng Huang. 2021b. Learning Based Proximity Matrix Factorization for Node Embedding. In SIGKDD. 2243--2253.","DOI":"10.1145\/3447548.3467296"},{"key":"e_1_3_2_1_99_1","doi-asserted-by":"crossref","unstructured":"Ziwei Zhang Peng Cui Xiao Wang Jian Pei Xuanrong Yao and Wenwu Zhu. 2018. Arbitrary-order proximity preserved network embedding. 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Beyond homophily in graph neural networks: Current limitations and effective designs. In NeurIPS."},{"key":"e_1_3_2_1_103_1","doi-asserted-by":"crossref","unstructured":"Meiqi Zhu Xiao Wang Chuan Shi Houye Ji and Peng Cui. 2021. Interpreting and unifying graph neural networks with an optimization framework. 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