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Appl."},{"key":"10.1016\/j.patcog.2026.114384_b79","doi-asserted-by":"crossref","DOI":"10.1016\/j.inffus.2023.102049","article-title":"Asset pricing via fused deep learning with visual clues","volume":"102","author":"Tan","year":"2024","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.patcog.2026.114384_b80","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.neucom.2022.04.105","article-title":"GPM: A graph convolutional network based reinforcement learning framework for portfolio management","volume":"498","author":"Shi","year":"2022","journal-title":"Neurocomputing"},{"key":"10.1016\/j.patcog.2026.114384_b81","doi-asserted-by":"crossref","unstructured":"P. Zhu, Y. Li, Y. Hu, Q. Liu, D. Cheng, Y. Liang, Lsr-igru: Stock trend prediction based on long short-term relationships and improved gru, in: Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, 2024, pp. 5135\u20135142.","DOI":"10.1145\/3627673.3680012"},{"key":"10.1016\/j.patcog.2026.114384_b82","article-title":"AC-HGL: Heterogeneous graph representation learning through adaptive correlation for stock movement prediction","author":"Yang","year":"2026","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patcog.2026.114384_b83","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2023.111326","article-title":"A framework for stock selection via concept-oriented attention representation in hypergraph neural network","volume":"284","author":"Yan","year":"2024","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.patcog.2026.114384_b84","doi-asserted-by":"crossref","DOI":"10.1016\/j.inffus.2024.102468","article-title":"Relational fusion-based stock selection with neural recursive ordinary differential equation networks","volume":"110","author":"Gao","year":"2024","journal-title":"Inf. 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Chen, Progressive Dependency Representation Learning for Stock Ranking in Uncertain Risk Contrasting, in: Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V. 1, 2025, pp. 532\u2013543.","DOI":"10.1145\/3690624.3709189"},{"key":"10.1016\/j.patcog.2026.114384_b91","doi-asserted-by":"crossref","unstructured":"J. Yoo, Y. Soun, Y.-c. Park, U. Kang, Accurate multivariate stock movement prediction via data-axis transformer with multi-level contexts, in: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, 2021, pp. 2037\u20132045.","DOI":"10.1145\/3447548.3467297"},{"issue":"8","key":"10.1016\/j.patcog.2026.114384_b92","doi-asserted-by":"crossref","first-page":"10539","DOI":"10.1109\/TNNLS.2023.3242473","article-title":"Projective incomplete multi-view clustering","volume":"35","author":"Deng","year":"2023","journal-title":"IEEE Trans. Neural Networks Learn. 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