GNN model的特征嵌入能力,其实可以理解为,不存在任何拓扑结构信息的情况下,GNN model对于node/edge/graph features的representation能力,是否能够使得node/edge/graph features 不相同的两张graph 产生不同的representation或者对于 node/edge/graph features不同的两张graph产生相似的representation; 实际上,这里已经脱离了gra...
Boukerche & Wang (2020a)对交通预测的机器学习模型进行了进一步的分类,包括回归模型、基于样本的模型(如k近邻)、基于核的模型(如支持向量机和径向基函数)、神经网络模型和混合模型。 Lee et al.(2021)进一步将深度学习模型分为5代,其中GCNs被划分为第四代,其他已经考虑但尚未广泛应用的先进技术被合并到第五代。
Note that we present a comprehensive comparison between different techniques and identify the pros and cons of various evaluation metrics in this survey.doi:10.1007/s10462-024-10808-0Wang, KunzeDing, YihaoHan, Soyeon CarenSpringer NetherlandsArtificial Intelligence Review...
此外,范围图分区保证每个分区都包含连续编号的顶点,从而降低了CPU和GPU之间的数据移动成本。Wang等人[102]使用E.q.8来估计分区的计算成本,并应用应用程序驱动的图分区方法[31]来生成工作负载平衡计划,该计划自适应地减轻工作负载并最小化通信成本。基于顶点切割分区,Hoang等人[48]利用2D笛卡尔顶点切割来提高可扩展性。
[13] P. Cui, X. Wang, J. Pei, and W. Zhu, “A survey on network em-bedding,” IEEE Transactions on Knowledge and Data Engineering, 2017. [14] W. L. Hamilton, R. Ying, and J. Leskovec, “Representation learn-ing on graphs: Methods and applications,” in Advances in Neural Infor...
From raw detector activations to reconstructed particles, data at the Large Hadron Collider (LHC) are sparse, irregular, heterogeneous and highly relational in nature. Graph neural networks (GNNs), a class of algorithms belonging to the rapidly growing f
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-Based EEG Classification: A Survey Graph neural networks (GNN) are increasingly used to classify EEG for tasks such as emotion recognition, motor imagery and neurological diseases and disord... D Klepl,M Wu,F He - 《IEEE Transactions on Neural Systems & Rehabilitation Enginee...
(GGNN) have demonstrated ground-breaking performance on many tasks mentioned above. In this survey, we provide a detailed review over existing graph neural network models, systematically categorize the applications, and propose four open problems for future research.大量的学习任务需要处理包含丰富元素间...
卷积神经网络 (convolutional neural network, CNNs) 平移不变性【目标无论平移到哪,其标签不变】 局部连通性 合成性 递归神经网络 (recurrent neural network, RNNs) 自编码器 (autoencoders, AE) 深度学习取得成功的两点原因 强大的计算资源 大量可用的数据 ...