Applications of Graph Neural Networks

最新推荐文章于 2024-03-15 16:29:03 发布

Adam婷

最新推荐文章于 2024-03-15 16:29:03 发布

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图形神经网络（GNNs）通过节点间的消息传递捕获图形依赖性，适用于各种领域，如物理系统、化学、图像分类、文本分析和组合优化。在物理系统中，GNN能模拟复杂的物理系统并预测物体轨迹；在化学中，GNN有助于药物设计；在图像分类中，GNN在零样本和少样本学习任务中有潜力；在文本处理中，通过将文本转化为结构化数据进行分析。GNNs在组合优化问题中也有用武之地，如解决最小顶点覆盖问题等。随着新架构的发展，GNNs的应用将更加广泛。

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Applications of Graph Neural Networks

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Graphs and their study have received a lot of attention since ages due to their ability of representing the real world in a fashion that can be analysed objectively. Indeed, graphs can be used to represent a lot of useful, real world datasets such as social networks, web link data, molecular structures, geographical maps, etc. Apart from these cases which have a natural structure to them, non-structured data such as images and text can also be modelled in the form of graphs in order to perform graph analysis on them. Due to the expressiveness of graphs and a tremendous increase