deep-learninggraph-generationexplainable-mlself-supervised-learning3d-graphgraph-neural-network UpdatedJul 15, 2024 Python FighterLYL/GraphNeuralNetwork Star1.8k Code Issues Pull requests 《深入浅出图神经网络:GNN原理解析》配套代码 gcngnngraph-neural-network ...
因此,本文试图沿着图神经网络的历史脉络,从最早基于不动点理论的图神经网络(Graph Neural Network, GNN)一步步讲到当前用得最火的图卷积神经网络(Graph Convolutional Neural Network, GCN), 期望通过本文带给读者一些灵感与启示。 本文的提纲与叙述要点主要参考了3篇图神经网络的Survey,分别是来自IEEE Fellow的A Comp...
The graph neural network model. Franco Scarselli,Marco Gori,Ah Chung Tsoi,Markus Hagenbuchner, Gabriele Monfardini.2009. citeseerx.ist.psu.edu/v Spectral networks and locally connected networks on graphs. Joan Bruna, Wojciech Zaremba, Arthur Szlam, Yann LeCun. ICLR 2014. arxiv.org/pdf/1312.6...
Graph Wavelet Neural Network Supervised Community Detection with Line Graph Neural Networks Predict then Propagate: Graph Neural Networks meet Personalized PageRank Invariant and Equivariant Graph Networks Capsule Graph Neural Network ICML 2019 MixHop: Higher-Order Graph Convolutional Architectures via Sparsified...
近日,清华大学孙茂松组在 arXiv 上发布预印版综述文章 Graph Neural Networks: A Review of Methods and Applications。该文总结了近年来 图神经网络领域的经典模型与典型应用,并提出了 2020 图神经网络 图神经网络 神经网络 结构化 Network 转载 智慧编织者 2023-11-17 22:26:23 43阅读 1 2 3 4 5...
Shen, “Graph contrastive learning with augmentations,”Advances in Neural Information Processing Systems, vol. 33, 2020. [2] J. Qiu, Q. Chen, Y. Dong, J. Zhang, H. Yang, M. Ding, K. Wang, and J. Tang, “Gcc: Graph contrastive coding for graph neural network pre-training,” in...
https://github.com/LirongWu/awesome-graph-self-supervised-learning 近些年来,图上的深度学习在各种任务上取得了显著的成功,而这种成功在很大程度上依赖于海量的、精心标注的数据。然而,精确的标注通常非常昂贵和耗时。为了解决这个问题,自监督学习(Self-supervised Learning,SSL)正在成为一种全新的范式,通过精心设计的...
与Deep Graph Library (DGL)(Wang et al., 2018a) 相比,PyG 训练模型的速度快了 15 倍。 表4:训练 runtime 比较 安装、教程&示例 PyTorch Geometric 使实现图卷积网络变得非常容易 (请参阅 GitHub 上的教程)。 例如,这就是实现一个边缘卷积层 (edge convolution layer) 所需的全部代码: import torchfrom...
Graph Isomorphism Network (GIN) from Xu et al. (2019) Approximate Personalized Propagation of Neural Predictions (APPNP) operator (Klicpera et al., 2019) 对于学习具有多维边缘特征的点云,流形和图,我们提供了: Schlichtkrull et al. (2018) 的 relationalGCNoperator ...
https://github.com/LirongWu/awesome-graph-self-supervised-learning 近些年来,图上的深度学习在各种任务上取得了显著的成功,而这种成功在很大程度上依赖于海量的、精心标注的数据。然而,精确的标注通常非常昂贵和耗时。为了解决这个问题,自监督学习(Self-supervised Learning,SSL)正在成为一种全新的范式,通过精心设计的...