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 graph sampling based inductive learning method that improves training efficiency and accuracy in a fundamentally different way. By changing perspective, GraphSAINT constructs minibatches by sampling the training graph, rather than the nodes or edges across GCN layers. Each iteration, a complete GCN is built from the properly sampled subgraph. Thus, we ensure fixed number of well-connected nodes in all layers. We further propose normalization technique to eliminate bias, and sampling algorithms for variance reduction. Importantly, we can decouple the sampling from the forward and backward propagation, and extend GraphSAINT with many architecture variants (e.g., graph attention, jumping connection). GraphSAINT demonstrates superior performance in both accuracy and training time on five large graphs, and achieves new state-of-the-art F1 scores for PPI (0.995) and Reddit (0.970).

Graph convolutional network (GCN) has been successfully applied to many graph-based applications; however, training a large-scale GCN remains challenging. Current SGD-based algorithms suffer from either a high computational cost that exponentially grows with number of GCN layers, or a large space requirement for keeping the entire graph and the embedding of each node in memory. In this paper, we propose Cluster-GCN, a novel GCN algorithm that is suitable for SGD-based training by exploiting the graph clustering structure. Cluster-GCN works as the following: at each step, it samples a block of nodes that associate with a dense subgraph identified by a graph clustering algorithm, and restricts the neighborhood search within this subgraph. This simple but effective strategy leads to significantly improved memory and computational efficiency while being able to achieve comparable test accuracy with previous algorithms. To test the scalability of our algorithm, we create a new Amazon2M data with 2 million nodes and 61 million edges which is more than 5 times larger than the previous largest publicly available dataset (Reddit). For training a 3-layer GCN on this data, Cluster-GCN is faster than the previous state-of-the-art VR-GCN (1523 seconds vs 1961 seconds) and using much less memory (2.2GB vs 11.2GB). Furthermore, for training 4 layer GCN on this data, our algorithm can finish in around 36 minutes while all the existing GCN training algorithms fail to train due to the out-of-memory issue. Furthermore, Cluster-GCN allows us to train much deeper GCN without much time and memory overhead, which leads to improved prediction accuracy-using a 5-layer Cluster-GCN, we achieve state-of-the-art test F1 score 99.36 on the PPI dataset, while the previous best result was 98.71 by [16]. Our codes are publicly available at https://github.com/google-research/google-research/ tree/master/cluster_gcn.

图形神经网络（GNN）在学习强大的节点表示中显示了令人信服的性能，这些表现在保留节点属性和图形结构信息的强大节点表示中。然而，许多GNNS在设计有更深的网络结构或手柄大小的图形时遇到有效性和效率的问题。已经提出了几种采样算法来改善和加速GNN的培训，但他们忽略了解GNN性能增益的来源。图表数据中的信息的测量可以帮助采样算法来保持高价值信息，同时消除冗余信息甚至噪声。在本文中，我们提出了一种用于GNN的公制引导（MEGUIDE）子图学习框架。 MEGUIDE采用两种新颖的度量：功能平滑和连接失效距离，以指导子图采样和迷你批次的培训。功能平滑度专为分析节点的特征而才能保留最有价值的信息，而连接失败距离可以测量结构信息以控制子图的大小。我们展示了MEGUIDE在多个数据集上培训各种GNN的有效性和效率。

Low-dimensional embeddings of nodes in large graphs have proved extremely useful in a variety of prediction tasks, from content recommendation to identifying protein functions. However, most existing approaches require that all nodes in the graph are present during training of the embeddings; these previous approaches are inherently transductive and do not naturally generalize to unseen nodes. Here we present GraphSAGE, a general inductive framework that leverages node feature information (e.g., text attributes) to efficiently generate node embeddings for previously unseen data. Instead of training individual embeddings for each node, we learn a function that generates embeddings by sampling and aggregating features from a node's local neighborhood. Our algorithm outperforms strong baselines on three inductive node-classification benchmarks: we classify the category of unseen nodes in evolving information graphs based on citation and Reddit post data, and we show that our algorithm generalizes to completely unseen graphs using a multi-graph dataset of protein-protein interactions. * The two first authors made equal contributions. 1 While it is common to refer to these data structures as social or biological networks, we use the term graph to avoid ambiguity with neural network terminology.

Recent deep learning approaches for representation learning on graphs follow a neighborhood aggregation procedure. We analyze some important properties of these models, and propose a strategy to overcome those. In particular, the range of "neighboring" nodes that a node's representation draws from strongly depends on the graph structure, analogous to the spread of a random walk. To adapt to local neighborhood properties and tasks, we explore an architecture -jumping knowledge (JK) networks -that flexibly leverages, for each node, different neighborhood ranges to enable better structure-aware representation. In a number of experiments on social, bioinformatics and citation networks, we demonstrate that our model achieves state-of-the-art performance. Furthermore, combining the JK framework with models like Graph Convolutional Networks, GraphSAGE and Graph Attention Networks consistently improves those models' performance.

图形神经网络（GNN）在许多基于图的应用程序中取得了巨大成功。但是，巨大的尺寸和高稀疏度的图表阻碍了其在工业场景下的应用。尽管为大规模图提出了一些可扩展的GNN，但它们为每个节点采用固定的$ k $ hop邻域，因此在稀疏区域内采用大型繁殖深度时面临过度光滑的问题。为了解决上述问题，我们提出了一种新的GNN体系结构 - 图形注意多层感知器（GAMLP），该架构可以捕获不同图形知识范围之间的基本相关性。我们已经与天使平台部署了GAMLP，并进一步评估了现实世界数据集和大规模工业数据集的GAMLP。这14个图数据集的广泛实验表明，GAMLP在享有高可扩展性和效率的同时，达到了最先进的性能。具体来说，在我们的大规模腾讯视频数据集上的预测准确性方面，它的表现优于1.3 \％，同时达到了高达$ 50 \ times $ triending的速度。此外，它在开放图基准的最大同质和异质图（即OGBN-PAPERS100M和OGBN-MAG）的排行榜上排名第一。

Neural message passing algorithms for semi-supervised classification on graphs have recently achieved great success. However, for classifying a node these methods only consider nodes that are a few propagation steps away and the size of this utilized neighborhood is hard to extend. In this paper, we use the relationship between graph convolutional networks (GCN) and PageRank to derive an improved propagation scheme based on personalized PageRank. We utilize this propagation procedure to construct a simple model, personalized propagation of neural predictions (PPNP), and its fast approximation, APPNP. Our model's training time is on par or faster and its number of parameters on par or lower than previous models. It leverages a large, adjustable neighborhood for classification and can be easily combined with any neural network. We show that this model outperforms several recently proposed methods for semi-supervised classification in the most thorough study done so far for GCN-like models. Our implementation is available online. 1

图形神经网络（GNN）已被证明是分析非欧国人图数据的强大工具。但是，缺乏有效的分布图学习（GL）系统极大地阻碍了GNN的应用，尤其是当图形大且GNN相对深时。本文中，我们提出了GraphTheta，这是一种以顶点为中心的图形编程模型实现的新颖分布式和可扩展的GL系统。 GraphTheta是第一个基于分布式图处理的GL系统，其神经网络运算符以用户定义的功能实现。该系统支持多种培训策略，并在分布式（虚拟）机器上启用高度可扩展的大图学习。为了促进图形卷积实现，GraphTheta提出了一个名为NN-Tgar的新的GL抽象，以弥合图形处理和图形深度学习之间的差距。提出了分布式图引擎，以通过混合平行执行进行随机梯度下降优化。此外，除了全球批次和迷你批次外，我们还为新的集群批次培训策略提供了支持。我们使用许多网络大小的数据集评估GraphTheta，范围从小，适度到大规模。实验结果表明，GraphTheta可以很好地扩展到1,024名工人，用于培训内部开发的GNN，该工业尺度的Aripay数据集为14亿个节点和41亿个属性边缘，并带有CPU虚拟机（Dockers）群的小群。 （5 $ \ sim $ 12GB）。此外，GraphTheta比最先进的GNN实现获得了可比或更好的预测结果，证明其学习GNN和现有框架的能力，并且可以超过多达$ 2.02 \ tims $ $ 2.02 \ times $，具有更好的可扩展性。据我们所知，这项工作介绍了文献中最大的边缘属性GNN学习任务。

Using graph neural networks for large graphs is challenging since there is no clear way of constructing mini-batches. To solve this, previous methods have relied on sampling or graph clustering. While these approaches often lead to good training convergence, they introduce significant overhead due to expensive random data accesses and perform poorly during inference. In this work we instead focus on model behavior during inference. We theoretically model batch construction via maximizing the influence score of nodes on the outputs. This formulation leads to optimal approximation of the output when we do not have knowledge of the trained model. We call the resulting method influence-based mini-batching (IBMB). IBMB accelerates inference by up to 130x compared to previous methods that reach similar accuracy. Remarkably, with adaptive optimization and the right training schedule IBMB can also substantially accelerate training, thanks to precomputed batches and consecutive memory accesses. This results in up to 18x faster training per epoch and up to 17x faster convergence per runtime compared to previous methods.

图表表示学习是一种快速增长的领域，其中一个主要目标是在低维空间中产生有意义的图形表示。已经成功地应用了学习的嵌入式来执行各种预测任务，例如链路预测，节点分类，群集和可视化。图表社区的集体努力提供了数百种方法，但在所有评估指标下没有单一方法擅长，例如预测准确性，运行时间，可扩展性等。该调查旨在通过考虑算法来评估嵌入方法的所有主要类别的图表变体，参数选择，可伸缩性，硬件和软件平台，下游ML任务和多样化数据集。我们使用包含手动特征工程，矩阵分解，浅神经网络和深图卷积网络的分类法组织了图形嵌入技术。我们使用广泛使用的基准图表评估了节点分类，链路预测，群集和可视化任务的这些类别算法。我们在Pytorch几何和DGL库上设计了我们的实验，并在不同的多核CPU和GPU平台上运行实验。我们严格地审查了各种性能指标下嵌入方法的性能，并总结了结果。因此，本文可以作为比较指南，以帮助用户选择最适合其任务的方法。

图表上的表示学习（也称为图形嵌入）显示了其对一系列机器学习应用程序（例如分类，预测和建议）的重大影响。但是，现有的工作在很大程度上忽略了现代应用程序中图和边缘的属性（或属性）中包含的丰富信息，例如，属性图表示的节点和边缘。迄今为止，大多数现有的图形嵌入方法要么仅关注具有图形拓扑的普通图，要么仅考虑节点上的属性。我们提出了PGE，这是一个图形表示学习框架，该框架将节点和边缘属性都包含到图形嵌入过程中。 PGE使用节点聚类来分配偏差来区分节点的邻居，并利用多个数据驱动的矩阵来汇总基于偏置策略采样的邻居的属性信息。 PGE采用了流行的邻里聚合归纳模型。我们通过显示PGE如何实现更好的嵌入结果的详细分析，并验证PGE的性能，而不是最新的嵌入方法嵌入方法在基准应用程序上的嵌入方法，例如节点分类和对现实世界中的链接预测数据集。

数据处理的最新进展刺激了对非常大尺度的学习图的需求。众所周知，图形神经网络（GNN）是解决图形学习任务的一种新兴和有力的方法，很难扩大规模。大多数可扩展模型应用基于节点的技术来简化GNN的昂贵图形消息传播过程。但是，我们发现当应用于百万甚至数十亿尺度的图表时，这种加速度不足。在这项工作中，我们提出了Scara，这是一种可扩展的GNN，具有针对图形计算的特征优化。 Scara有效地计算出从节点功能中嵌入的图形，并进一步选择和重用功能计算结果以减少开销。理论分析表明，我们的模型在传播过程以及GNN培训和推理中具有确保精度，实现了子线性时间的复杂性。我们在各种数据集上进行了广泛的实验，以评估圣aca的功效和效率。与基线的性能比较表明，与快速收敛和可比精度相比，与当前的最新方法相比，圣aca最高可达到100倍的图形传播加速度。最值得注意的是，在100秒内处理最大的十亿个GNN数据集纸100m（1.11亿节点，1.6B边缘）上的预先计算是有效的。

近年来，异构图形神经网络（HGNNS）一直在开花，但每个工作所使用的独特数据处理和评估设置会让他们的进步完全了解。在这项工作中，我们通过使用其官方代码，数据集，设置和超参数来展示12个最近的HGNN的系统再现，揭示了关于HGNN的进展的令人惊讶的结果。我们发现，由于设置不当，简单的均匀GNN，例如GCN和GAT在很大程度上低估了。具有适当输入的GAT通常可以匹配或优于各种场景的所有现有HGNN。为了促进稳健和可重复的HGNN研究，我们构建异构图形基准（HGB），由具有三个任务的11个不同数据集组成。 HGB标准化异构图数据分割，特征处理和性能评估的过程。最后，我们介绍了一个简单但非常强大的基线简单 - HGN - 这显着优于HGB上以前的所有模型 - 以加速未来HGNN的进步。

图表卷积网络（GCNS）在各种半监督节点分类任务中取得了令人印象深刻的实证进步。尽管取得了巨大的成功，但在大型图形上培训GCNS遭受了计算和内存问题。规避这些障碍的潜在路径是基于采样的方法，其中在每个层处采样节点的子集。虽然最近的研究已经证明了基于采样的方法的有效性，但这些作品缺乏在现实环境下的理论融合担保，并且不能完全利用优化期间演出参数的信息。在本文中，我们描述并分析了一般的双差异减少模式，可以在内存预算下加速任何采样方法。所提出的模式的激励推动是仔细分析采样方法的差异，其中示出了诱导方差可以在前进传播期间分解为节点嵌入近似方差（Zeroth阶差异）（第一 - 顺序变化）在后向传播期间。理论上，从理论上分析所提出的架构的融合，并显示它享有$ \ Mathcal {O}（1 / T）$收敛率。我们通过将建议的模式集成在不同的采样方法中并将其应用于不同的大型实际图形来补充我们的理论结果。

近年来，图表表示学习越来越多地引起了越来越长的关注，特别是为了在节点和图表水平上学习对分类和建议任务的低维嵌入。为了能够在现实世界中的大规模图形数据上学习表示，许多研究专注于开发不同的抽样策略，以方便培训过程。这里，我们提出了一种自适应图策略驱动的采样模型（GPS），其中通过自适应相关计算实现了本地邻域中每个节点的影响。具体地，邻居的选择是由自适应策略算法指导的，直接贡献到消息聚合，节点嵌入更新和图级读出步骤。然后，我们从各种角度对图表分类任务进行全面的实验。我们所提出的模型在几个重要的基准测试中优于现有的3％-8％，实现了现实世界数据集的最先进的性能。

Graph convolutional networks (GCNs) are a powerful deep learning approach for graph-structured data. Recently, GCNs and subsequent variants have shown superior performance in various application areas on real-world datasets. Despite their success, most of the current GCN models are shallow, due to the over-smoothing problem.In this paper, we study the problem of designing and analyzing deep graph convolutional networks. We propose the GCNII, an extension of the vanilla GCN model with two simple yet effective techniques: Initial residual and Identity mapping. We provide theoretical and empirical evidence that the two techniques effectively relieves the problem of over-smoothing. Our experiments show that the deep GCNII model outperforms the state-of-the-art methods on various semi-and fullsupervised tasks. Code is available at https: //github.com/chennnM/GCNII.

提高GCN的深度（预计将允许更多表达性）显示出损害性能，尤其是在节点分类上。原因的主要原因在于过度平滑。过度平滑的问题将GCN的输出驱动到一个在节点之间包含有限的区别信息的空间，从而导致表现不佳。已经提出了一些有关完善GCN架构的作品，但理论上仍然未知这些改进是否能够缓解过度平衡。在本文中，我们首先从理论上分析了通用GCN如何与深度增加的作用，包括通用GCN，GCN，具有偏见，RESGCN和APPNP。我们发现所有这些模型都以通用过程为特征：所有节点融合到Cuboid。在该定理下，我们建议通过在每个训练时期随机去除一定数量的边缘来减轻过度光滑的状态。从理论上讲，Dropedge可以降低过度平滑的收敛速度，或者可以减轻尺寸崩溃引起的信息损失。对模拟数据集的实验评估已可视化不同GCN之间过度平滑的差异。此外，对几个真正的基准支持的广泛实验，这些实验始终如一地改善各种浅GCN和深度GCN的性能。

大规模淋巴结分类的图形神经网络（GNNS）培训具有挑战性。关键困难在于在避免邻居爆炸问题的同时获得准确的隐藏节点表示。在这里，我们提出了一种新技术，称为特征动量（FM），该技术在更新功能表示时使用动量步骤来合并历史嵌入。我们开发了两种特定的算法，称为GraphFM-IB和GraphFM-OB，它们分别考虑了内部和隔离外数据。 GraphFM-AIB将FM应用于内部采样数据，而GraphFM-OB则将FM应用于隔离数据的隔离数据，而口气数据是1跳入数据的1个邻域。对于特征嵌入的估计误差，我们为GraphFM-IB和GraphFM-OB的理论见解提供了严格的合并分析。从经验上讲，我们观察到GraphFM-IB可以有效缓解现有方法的邻里爆炸问题。此外，GraphFM-OB在多个大型图形数据集上实现了有希望的性能。

大规模图在现实情况下无处不在，可以通过图神经网络（GNN）训练以生成下游任务的表示形式。鉴于大规模图的丰富信息和复杂的拓扑结构，我们认为在这样的图中存在冗余，并将降低训练效率。不幸的是，模型可伸缩性严重限制了通过香草GNNS训练大规模图的效率。尽管在基于抽样的培训方法方面取得了最新进展，但基于抽样的GNN通常忽略了冗余问题。在大规模图上训练这些型号仍然需要无法容忍的时间。因此，我们建议通过重新思考图中的固有特征来降低冗余并提高使用GNN的大规模训练效率。在本文中，我们开拓者提出了一种称为dropreef的曾经使用的方法，以在大规模图中删除冗余。具体而言，我们首先进行初步实验，以探索大规模图中的潜在冗余。接下来，我们提出一个度量标准，以量化图中所有节点的异质性。基于实验和理论分析，我们揭示了大规模图中的冗余，即具有高邻居异质的节点和大量邻居。然后，我们建议Dropreef一劳永逸地检测并删除大规模图中的冗余，以帮助减少训练时间，同时确保模型准确性没有牺牲。为了证明DropReef的有效性，我们将其应用于最新的基于最新的采样GNN，用于训练大规模图，这是由于此类模型的高精度。使用Dropreef杠杆，可以大力提高模型的训练效率。 Dropreef高度兼容，并且在离线上执行，从而在很大程度上使目前和未来的最新采样GNN受益。

Users' involvement in creating and propagating news is a vital aspect of fake news detection in online social networks. Intuitively, credible users are more likely to share trustworthy news, while untrusted users have a higher probability of spreading untrustworthy news. In this paper, we construct a dual-layer graph (i.e., the news layer and the user layer) to extract multiple relations of news and users in social networks to derive rich information for detecting fake news. Based on the dual-layer graph, we propose a fake news detection model named Us-DeFake. It learns the propagation features of news in the news layer and the interaction features of users in the user layer. Through the inter-layer in the graph, Us-DeFake fuses the user signals that contain credibility information into the news features, to provide distinctive user-aware embeddings of news for fake news detection. The training process conducts on multiple dual-layer subgraphs obtained by a graph sampler to scale Us-DeFake in large scale social networks. Extensive experiments on real-world datasets illustrate the superiority of Us-DeFake which outperforms all baselines, and the users' credibility signals learned by interaction relation can notably improve the performance of our model.