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Inductive relation reasoning for knowledge graphs, aiming to infer missing links between brand-new entities, has drawn increasing attention.
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K. Toutanova, D. Chen, P. Pantel, H. Poon, P. Choudhury, and M. Gamon, “Representing text for joint embedding of text and knowledge bases,” in Proceedings of the 2015 conference on empirical methods in natural language processing . Lisbon, Portugal: Association for Computational Linguistics, 2015, pp. 1499–1509
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2017
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2017
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R. Das, A. Neelakantan, D. Belanger, and A. McCallum, “Chains of reasoning over entities, relations, and text using recurrent neural networks,” in Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers , 2017, pp. 132–141
2017
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W. L. Hamilton, R. Ying, and J. Leskovec, “Inductive representation learning on large graphs,” in Proceedings of the 31st International Conference on Neural Information Processing Systems . Red Hook, NY, USA: Curran Associates Inc., 2017, pp. 1025–1035
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2017
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H. Wang, F. Zhang, J. Wang, M. Zhao, W. Li, X. Xie, and M. Guo, “Ripplenet: Propagating user preferences on the knowledge graph for recommender systems,” in Proceedings of the 27th ACM international conference on information and knowledge management . Torino, Italy: Association for Computing Machinery, 2018, pp. 417–426
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2018
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M. Schlichtkrull, T. N. Kipf, P. Bloem, R. v. d. Berg, I. Titov, and M. Welling, “Modeling relational data with graph convolutional networks,” in European semantic web conference . Aldemar Knossos Royal Village Conference Centre: Springer, 2018, pp. 593–607
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2018
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2021
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S. Mai, S. Zheng, Y. Yang, and H. Hu, “Communicative message passing for inductive relation reasoning.” in The Thirty-Fifth AAAI Conference on Artificial Intelligence . virtually: AAAI Press, 2021, pp. 4294–4302
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2018
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H. Chen, H. Yin, W. Wang, H. Wang, Q. V. H. Nguyen, and X. Li, “Pme: Projected metric embedding on heterogeneous networks for link prediction,” in Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery , ser. KDD ’18. New York, NY, USA: Association for Computing Machinery, 2018, p. 1177–1186. [Online]. Available: https://doi.org/10.1145/3219819.3219986
2018
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P. Veličković, G. Cucurull, A. Casanova, A. Romero, P. Liò, and Y. Bengio, “Graph attention networks,” in International Conference on Learning Representations . Vancouver CANADA: Openreview.Net, 2018
2018
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A. Sadeghian, M. Armandpour, P. Ding, and D. Z. Wang, “Drum: End-to-end differentiable rule mining on knowledge graphs,” Advances in Neural Information Processing Systems , vol. 32, 2019
2019
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Y. Cen, X. Zou, J. Zhang, H. Yang, J. Zhou, and J. Tang, “Representation learning for attributed multiplex heterogeneous network,” in Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . New York, NY, USA: Association for Computing Machinery, 2019, pp. 1358–1368
2019
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Y. Feng, H. You, Z. Zhang, R. Ji, and Y. Gao, “Hypergraph neural networks,” in Proceedings of the AAAI conference on artificial intelligence . Honolulu, Hawaii, USA: AAAI Press, 2019, pp. 3558–3565
2019
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S. Vashishth, S. Sanyal, V. Nitin, and P. Talukdar, “Composition-based multi-relational graph convolutional networks,” in International Conference on Learning Representations . New Orleans, LA, USA: Openreview.Net, 2019
2019
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J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of deep bidirectional transformers for language understanding,” in Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) . Minneapolis, Minnesota: Association for Computational Linguistics, Jun. 2019, pp. 4171–4186. [Online]. Available: https://aclanthology.org/N19-1423
2019
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A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga et al. , “Pytorch: An imperative style, high-performance deep learning library,” Advances in neural information processing systems , vol. 32, 2019
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2021
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2021
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J. Zhang, X. Zhang, J. Yu, J. Tang, J. Tang, C. Li, and H. Chen, “Subgraph retrieval enhanced model for multi-hop knowledge base question answering,” in Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) . Zilker Park, Austin: Association for Computational Linguistics, 2022, pp. 5773–5784
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W. Fan, X. Liu, W. Jin, X. Zhao, J. Tang, and Q. Li, “Graph trend filtering networks for recommendation,” in Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval , ser. SIGIR ’22. New York, NY, USA: Association for Computing Machinery, 2022, p. 112–121. [Online]. Available: https://doi.org/10.1145/3477495.3531985
2022
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S. Zheng, S. Mai, Y. Sun, H. Hu, and Y. Yang, “Subgraph-aware few-shot inductive link prediction via meta-learning,” IEEE Transactions on Knowledge and Data Engineering , vol. Early Access, 2022
2022
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H. Chen, Z. Huang, Y. Xu, Z. Deng, F. Huang, P. He, and Z. Li, “Neighbor enhanced graph convolutional networks for node classification and recommendation,” Knowledge-Based Systems , vol. 246, p. 108594, 2022
2022
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X. Xu, P. Zhang, Y. He, C. Chao, and C. Yan, “Subgraph neighboring relations infomax for inductive link prediction on knowledge graphs,” in Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence,IJCAI-22 . Vienna, Austria: International Joint Conferences on Artificial Intelligence Organization, 7 2022, pp. 2341–2347, main Track
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2023
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2023
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2023
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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 The world wide web conference . San Francisco, United States: Association for Computing Machinery, 2019, pp. 2022–2032
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