引言预发表版本:Unveiling the potential of Graph Neural Networks for network modeling and optimization in SDN 正刊(通信顶刊)IEEE Journal on Selected Areas in Communications:… 大大大狮几 《Sequential Recommendation with Graph Neural Networks》 摘要:基于用户历史行为的推荐工作已经有很多,但是并没有解决两...
In the context of neuroimaging, neural activity patterns can be interpreted as a graph structured spatio-temporal signal distribution. The nodes in this graph represent ROIs in a human brain, while the edges reflect the connection strengths between these ROIs in the anatomical neuronal network, whic...
Graph neural network (GNN) is an effective neural architecture for mining graph-structured data, since it can capture the high-order content and topological information on graphs12. It has been widely used in personalization scenarios such as product recommendation13,14,15and content recommendation16t...
例如,道路网络自然是一个graph,以道路交叉点为节点,道路连接点为边。以graph作为输入,一些基于gnn的模型在包括道路交通流和速度预测问题的任务中显示出比以前的方法更好的性能。 例如,这些模型包括扩散卷积递归神经网络(DCRNN) (Li等人,2018b)和Graph WaveNet (Wu等人,2019)模型。基于gnn的方法也被扩展到其他交通方...
Deep learning Graph neural network 1. Introduction Graphs are a kind of data structure which models a set of objects (nodes) and their relationships (edges). Recently, researches on analyzing graphs with machine learning have been receiving more and more attention because of the great expressive ...
Deep Convolutional Networks on Graph-Structured Data:谱域 Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering:谱域 Semi-Supervised Classification with Graph Convolutional Networks:谱域 空域论文# Neural Network for Graphs: A Contextual Constructive Approach:空域图卷积早期代表作品 ...
We construct a homogeneous graph neural network (GNN) based multi-agent deep deterministic policy gradient (herein HGNN-MADDPG) algorithm model for multi-agent flocking control system. In HGNN-MADDPG, we design a GNN with loss weight to complete the feature extraction of the structural information ...
2020s and beyond.Neural networks continue to undergo rapid development, with advancements in architecture, training methods and applications. Researchers are exploring new network structures such as transformers andgraph neural networks, which excel in NLP and understanding complex relationships. Additionally...
【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...
Li, Y., Gu, C., Dullien, T., Vinyals, O., Kohli, P.: Graph matching networks for learning the similarity of graph structured objects. In: Chaudhuri, K., Salakhutdinov, R. (eds.) Proceedings of the 36th International Conference on Machine Learning. Proceedings of Machine Learning Research...