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Multi-view graph convolutional networks with attention mechanism
Artificial Intelligence 307 (C):103708 (2022)
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Citations of this work
Dual-track spatio-temporal learning for urban flow prediction with adaptive normalization.Xiaoyu Li, Yongshun Gong, Wei Liu, Yilong Yin, Yu Zheng & Liqiang Nie - 2024 - Artificial Intelligence 328 (C):104065.
MATE: Masked optimal transport with dynamic selection for partial label graph learning.Yiyang Gu, Binqi Chen, Zihao Chen, Ziyue Qiao, Xiao Luo, Junyu Luo, Zhiping Xiao, Wei Ju & Ming Zhang - 2025 - Artificial Intelligence 348 (C):104396.
Polarized message-passing in graph neural networks.Tiantian He, Yang Liu, Yew-Soon Ong, Xiaohu Wu & Xin Luo - 2024 - Artificial Intelligence 331 (C):104129.
Towards context-aware graph representation learning: Adaptive node aggregation with LLMs.Songwei Zhao, Yuan Jiang, Sinuo Zhang, Jifeng Hu, Philip S. Yu & Hechang Chen - 2026 - Artificial Intelligence 358 (C):104584.
References found in this work
Optimal cruiser-drone traffic enforcement under energy limitation.Ariel Rosenfeld & Oleg Maksimov - 2019 - Artificial Intelligence 277 (C):103166.
Predicting citywide crowd flows using deep spatio-temporal residual networks.Junbo Zhang, Yu Zheng, Dekang Qi, Ruiyuan Li, Xiuwen Yi & Tianrui Li - 2018 - Artificial Intelligence 259 (C):147-166.
Distributional semantics of objects in visual scenes in comparison to text.Timo Lüddecke, Alejandro Agostini, Michael Fauth, Minija Tamosiunaite & Florentin Wörgötter - 2019 - Artificial Intelligence 274 (C):44-65.
Forming k coalitions and facilitating relationships in social networks.Liat Sless, Noam Hazon, Sarit Kraus & Michael Wooldridge - 2018 - Artificial Intelligence 259 (C):217-245.
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