In recent years, to model the graph structures in the transportation systems as well as the contextual information, graph neural networks (GNNs) are introduced as new tools and have achieved the state-of-the-art performance in a series of traffic forecasting problems. In this survey, we review...
虽然存在一些相关的交通预测调查(Boukerche等人,2020年;Boukerche & Wang, 2020a;Fan等人,2020年;乔治与桑特拉出版社,2020年;Haghighat等人,2020年;Lee等人,2021年;卢卡等人,2020年;Manibardo等人,2020年;帕夫,2019;Shi & Yeung, 2018;Tedjopurnomo等人,2020年;Varghese等人,2020年;谢等,2020a;叶等,2020a;Yin等人...
《The Expressive Power of Graph Neural Networks as a Query Language 》 sigmodrecord.org/public 大概的思路是,第i层聚合的时候,节点v先按照正常的方式聚合第i-1层的邻居的node representations,得到一个vector,然后又聚合了第i层的邻居的node representations,得到一个vector,然后concat。 奇怪的操作。 图拓扑增...
The graph neural network (GNN), as a new type of neural network, has been proposed to extract features from non-Euclidean space data. Motivated by CNN, a GNN enables the use of a scalable kernel to perform convolutions on non-Euclidean space data. To achieve the convolution operation in ...
Machine learning plays an increasingly important role in many areas of chemistry and materials science, being used to predict materials properties, accelerate simulations, design new structures, and predict synthesis routes of new materials. Graph neural
Graph neural network (GNN) is effective in modeling high-order interactions and has been widely used in various personalized applications such as recommendation. However, mainstream personalization methods rely on centralized GNN learning on global graphs, which have considerable privacy risks due to the...
【阅读】A Comprehensive Survey on Distributed Training of Graph Neural Networks 摘要 图神经网络(GNNs)是一种在图上学习的深度学习模型,并已成功应用于许多领域。尽管 GNN 有效,但GNN 有效地扩展到大型图仍然具有挑战性。作为一种补救措施,分布式计算成为训练大规模 GNN 的一种有前途的解决方案,因为它能够提供丰...
Wang等人[102]使用多项式函数𝑓 以在一组手动选择的特征上估计顶点的计算成本。正式定义为 其中,𝑇 是由GNN模型定义的邻居类型(例如元路径[102])的数量,𝑛𝑖 是𝑖-th型,𝑚𝑖 是𝑖-第种类型的邻居实例(即𝑖-th类型具有𝑛 顶点,每个顶点都有特征尺寸𝑓 , 然后𝑚𝑖 = 𝑛 × 𝑓 ). ...
Suhang Wang Link of the survey:https://arxiv.org/pdf/2204.08570.pdf Graphs-structured data are pervasive in real-world such as social networks, traffic networks, knowledge graph and protein-protein interaction networks. Graph Neural Networks (GNNs) extend neural networks for...
A Comprehensive Survey on Graph Neural Networks(图神经网络综合研究),程序员大本营,技术文章内容聚合第一站。