graphsaint是怎么采样的 nodesampler edgesampler rw和mrw sampler 直观的理解以及与cluster gcn的比较 和graphsage这类node-level sampling以及fastgcn这类layer-level sampling不同,graphsaint和clustergcn一样,属于graph-level的sampling方法。 和cluster-gcn不同,cluster-gcn中,使用的metis或graclus 切分出来的subgraph或者...
本文介绍发表在ICLR2020上的论文GraphSAINT: GRAPH SAMPLING BASED INDUCTIVE LEARNING METHOD》,它也是基于抽样的图神经网络框架。GraphSAINT引入一种基于抽样子图的图神经网络模型,每个minibatch上的计算在抽样子图上进行,不会出现”邻居爆炸“现象,同时作者给出的抽样方法在理论上有无偏性和最小方差。此外该模型把抽样和...
Graph Convolutional Networks (GCNs) are powerful models for learning representations of attributed graphs. To scale GCNs to large graphs, state-of-the-art methods use various layer sampling techniques to alleviate the "neighbor explosion" problem during minibatch training. We propose GraphSAINT, a gra...
Scalability: GraphSAINT achieves scalability w.r.t. 1).graph size: our subgraph size does not need to grow proportionally with the training graphs size. So even if we are dealing with a million-node graph, the subgraphs can still easily fit in the GPU memory; 2).model size: by resolving...
We have integrated the following architecture variants into GraphSAINT in this codebase: Higher order graph convolutional layers: Just specify the order in the configuration file (see ./train_config/README.md, and also ./train_config/explore/reddit2_o2_rw.yml for an example order two GCN reach...
GraphSAINT 提出了一种更通用的概率图采样器来构建小批量子图。可能的采样方案包括统一节点 / 边缘采样以及随机游走采样。然而,由于上一节中强调的可靠性问题(语义和梯度信息),与在全图上训练相比,子采样方法可能会限制模型的性能。 历史节点嵌入 GNNAutoScale (GAS) 是基本子采样技术的一种很有前景的替代方案,用于...
除了这篇“新颖的论文”,还有其他网友推荐的不同方向论文,如基于BERT提出的 ALBERT,通过减少参数来减少内存的消耗;关于 GNN 和 GCN 的论文《GraphSAINT: Graph Sampling Based Inductive Learning Method》、《Demystifying Graph Neural Network Via Graph Filter Assessment》;NAS 也是近两年的热点,研究者们推荐了多篇...
目录1.论文资料 2.传统GNN挑战:邻居爆炸(Neighbor Explosion) 3.现有方法:图采样 4.GraphSAINT:截然不同的采样的视角 4.1.算法流程 4.2.子图采样 4.3.实验结果:优于GCN, SAGE... 参考文献 1.论文资料 作者:曾涵清博士,南加州大学 论文:在 ICLR 2020 上发表了GraphSAINT: Graph Sampling Based... 查看原文 ...
本文发表在ICLR2020上的论文GraphSAINT: GRAPH SAMPLING BASED INDUCTIVE LEARNING METHOD》,它也是基于抽样的图神经网络框架。GraphSAINT引入一种基于抽样子图的图神经网络模型,每个minibatch上的计算在抽样子图上进行,不会出现”邻居爆炸“现象,同时作者给出的抽样方法在理论上有无偏性和最小方差。此外该模型把抽样和GNN...
[FPGA 2020] Open sourced implementation for the ACM/SIGDA FPGA '20 paper titled "GraphACT: Accelerating GCN Training on CPU-FPGA Heterogeneous Platforms" - GraphSAINT/GraphACT