基本信息
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个人简介
Research Interests
Graph machine learning. Many real-world problems are inherently graph-structured, e.g., social networks, biological networks, World Wide Web, molecules, circuits, brain, road networks, knowledge graphs, etc. Many machine learning algorithms are also defined on graphs, such as neural networks and graphical models. In this field, I develop algorithms and theories for learning over graphs, and apply them to problems like link prediction, graph classification, graph structure optimization, and knowledge graph reasoning. I am also interested in practical applications of graph neural networks, including brain modeling, drug discovery, healthcare, and biological applications.
Graph machine learning. Many real-world problems are inherently graph-structured, e.g., social networks, biological networks, World Wide Web, molecules, circuits, brain, road networks, knowledge graphs, etc. Many machine learning algorithms are also defined on graphs, such as neural networks and graphical models. In this field, I develop algorithms and theories for learning over graphs, and apply them to problems like link prediction, graph classification, graph structure optimization, and knowledge graph reasoning. I am also interested in practical applications of graph neural networks, including brain modeling, drug discovery, healthcare, and biological applications.
研究兴趣
论文共 80 篇作者统计合作学者相似作者
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CoRR (2024)
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arxiv(2024)
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Xiangbin Meng, Xiangyu Yan, Kuo Zhang, Da Liu, Xiaojuan Cui, Yaodong Yang,Muhan Zhang, Chunxia Cao,Jingjia Wang,Xuliang Wang,Jun gao, Yuan-geng-shuo Wang,
iSciencepp.109713, (2024)
CoRR (2024)
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arXiv (Cornell University) (2023)
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CoRR (2023)
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arxiv(2023)
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arxiv(2023)
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2
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