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Graph representation learning, a critical step in graph-centric tasks, has seen significant advancements.
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J. Kim, E. Kim, K. Yeo, Y. Jeon, C. Kim, S. Lee, and J. Lee, “Content-based graph reconstruction for cold-start item recommendation,” in SIGIR , 2024, pp. 1263–1273
2024
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J. Bo and Y. Fang, “Contrastive general graph matching with adaptive augmentation sampling,” in IJCAI , 2024, pp. 3724–3732
2024
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J. Tang, Y. Yang, W. Wei, L. Shi, L. Su, S. Cheng, D. Yin, and C. Huang, “Graphgpt: Graph instruction tuning for large language models,” in SIGIR , 2024, pp. 491–500
2024
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B. Hao, C. Yang, L. Guo, J. Yu, and H. Yin, “Motif-based prompt learning for universal cross-domain recommendation,” in WSDM , 2024, pp. 257–265
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2024
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2024
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T. Fang, Y. Zhang, Y. Yang, C. Wang, and L. Chen, “Universal prompt tuning for graph neural networks,” in NeurIPS , 2024
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2024
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2024
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J. Tang, Y. Yang, W. Wei, L. Shi, L. Xia, D. Yin, and C. Huang, “HiGPT: Heterogeneous graph language model,” in KDD , 2024, pp. 2842–2853
2024
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H. Zhao, A. Chen, X. Sun, H. Cheng, and J. Li, “All in one and one for all: A simple yet effective method towards cross-domain graph pretraining,” in KDD , 2024, pp. 4443–4454
2024
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X. Wang, H. Ji, C. Shi, B. Wang, Y. Ye, P. Cui, and P. S. Yu, “Heterogeneous graph attention network,” in WWW , 2019, pp. 2022–2032
2032
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Y. Zhu, Y. Xu, F. Yu, Q. Liu, S. Wu, and L. Wang, “Graph contrastive learning with adaptive augmentation,” in WWW , 2021, pp. 2069–2080
2080
Closest in time.