Graph neural networks (GNNs) have been demonstrated to be a powerful algorithmic model in broad application fields for their effectiveness in learning over graphs. To scale GNN training up for large-scale and ever-growing graphs, the most promising solution is distributed training which distributes the workload of training across multiple computing nodes. However, the workflows, computational patterns, communication patterns, and optimization techniques of distributed GNN training remain preliminarily understood. In this paper, we provide a comprehensive survey of distributed GNN training by investigating various optimization techniques used in distributed GNN training. First, distributed GNN training is classified into several categories according to their workflows. In addition, their computational patterns and communication patterns, as well as the optimization techniques proposed by recent work are introduced. Second, the software frameworks and hardware platforms of distributed GNN training are also introduced for a deeper understanding. Third, distributed GNN training is compared with distributed training of deep neural networks, emphasizing the uniqueness of distributed GNN training. Finally, interesting issues and opportunities in this field are discussed.
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Time series anomaly detection has applications in a wide range of research fields and applications, including manufacturing and healthcare. The presence of anomalies can indicate novel or unexpected events, such as production faults, system defects, or heart fluttering, and is therefore of particular interest. The large size and complex patterns of time series have led researchers to develop specialised deep learning models for detecting anomalous patterns. This survey focuses on providing structured and comprehensive state-of-the-art time series anomaly detection models through the use of deep learning. It providing a taxonomy based on the factors that divide anomaly detection models into different categories. Aside from describing the basic anomaly detection technique for each category, the advantages and limitations are also discussed. Furthermore, this study includes examples of deep anomaly detection in time series across various application domains in recent years. It finally summarises open issues in research and challenges faced while adopting deep anomaly detection models.
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Most existing deep learning models are trained based on the closed-world assumption, where the test data is assumed to be drawn i.i.d. from the same distribution as the training data, known as in-distribution (ID). However, when models are deployed in an open-world scenario, test samples can be out-of-distribution (OOD) and therefore should be handled with caution. To detect such OOD samples drawn from unknown distribution, OOD detection has received increasing attention lately. However, current endeavors mostly focus on grid-structured data and its application for graph-structured data remains under-explored. Considering the fact that data labeling on graphs is commonly time-expensive and labor-intensive, in this work we study the problem of unsupervised graph OOD detection, aiming at detecting OOD graphs solely based on unlabeled ID data. To achieve this goal, we develop a new graph contrastive learning framework GOOD-D for detecting OOD graphs without using any ground-truth labels. By performing hierarchical contrastive learning on the augmented graphs generated by our perturbation-free graph data augmentation method, GOOD-D is able to capture the latent ID patterns and accurately detect OOD graphs based on the semantic inconsistency in different granularities (i.e., node-level, graph-level, and group-level). As a pioneering work in unsupervised graph-level OOD detection, we build a comprehensive benchmark to compare our proposed approach with different state-of-the-art methods. The experiment results demonstrate the superiority of our approach over different methods on various datasets.
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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。
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在最新的联合学习研究(FL)的研究中,广泛采用了客户选择方案来处理沟通效率的问题。但是,从随机选择的非代表性子集汇总的模型更新的较大差异直接减慢了FL收敛性。我们提出了一种新型的基于聚类的客户选择方案,以通过降低方差加速FL收敛。简单而有效的方案旨在改善聚类效果并控制效果波动,因此,以采样的一定代表性生成客户子集。从理论上讲,我们证明了降低方差方案的改进。由于差异的差异,我们还提供了提出方法的更严格的收敛保证。实验结果证实了与替代方案相比,我们计划的效率超出了效率。
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异质图神经网络(HGNN)提供了强大的能力,可以将异质图的丰富结构和语义信息嵌入到低维节点表示中。现有的HGNN通常会学习使用层次结构注意机制和重复的邻居聚集来嵌入信息,并遭受不必要的复杂性和冗余计算。本文提出了简单有效的异质图神经网络(SEHGNN),该图通过避免在相同关系中避免过度使用的节点级别的注意来降低这种过度的复杂性,并在预处理阶段预先计算邻居聚集。与以前的工作不同,Sehgnn利用轻重量参数的邻居聚合器来学习每个Metapath的结构信息,以及一个基于变压器的语义聚合器将跨Metapaths的语义信息组合为每个节点的最终嵌入。结果,SEHGNN提供了简单的网络结构,高预测准确性和快速训练速度。在五个现实世界的异质图上进行了广泛的实验,证明了Sehgnn在准确性和训练速度上的优越性。代码可在https://github.com/ict-gimlab/sehgnn上找到。
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诸如学术文章和商业报告之类的长期文件一直是详细说明重要问题和需要额外关注的复杂主题的标准格式。自动汇总系统可以有效地将长文档置于简短而简洁的文本中,以封装最重要的信息,从而在帮助读者的理解中很重要。最近,随着神经体系结构的出现,已经做出了重大的研究工作,以推动自动文本摘要系统,以及有关将这些系统扩展到长期文档领域的挑战的大量研究。在这项调查中,我们提供了有关长期文档摘要的研究的全面概述,以及其研究环境的三个主要组成部分的系统评估:基准数据集,汇总模型和评估指标。对于每个组成部分,我们在长期汇总的背景下组织文献,并进行经验分析,以扩大有关当前研究进度的观点。实证分析包括一项研究基准数据集的内在特征,摘要模型的多维分析以及摘要评估指标的综述。根据总体发现,我们通过提出可能在这个快速增长的领域中提出未来探索的方向来得出结论。
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在异质图上的自我监督学习(尤其是对比度学习)方法可以有效地摆脱对监督数据的依赖。同时,大多数现有的表示学习方法将异质图嵌入到欧几里得或双曲线的单个几何空间中。这种单个几何视图通常不足以观察由于其丰富的语义和复杂结构而观察到异质图的完整图片。在这些观察结果下,本文提出了一种新型的自我监督学习方法,称为几何对比度学习(GCL),以更好地表示监督数据是不可用时的异质图。 GCL同时观察了从欧几里得和双曲线观点的异质图,旨在强烈合并建模丰富的语义和复杂结构的能力,这有望为下游任务带来更多好处。 GCL通过在局部局部和局部全球语义水平上对比表示两种几何视图之间的相互信息。在四个基准数据集上进行的广泛实验表明,在三个任务上,所提出的方法在包括节点分类,节点群集和相似性搜索在内的三个任务上都超过了强基础,包括无监督的方法和监督方法。
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图形相似性学习是指计算两个图之间的相似性得分,这在许多现实的应用程序(例如视觉跟踪,图形分类和协作过滤)中需要。由于大多数现有的图形神经网络产生了单个图的有效图表,因此几乎没有努力共同学习两个图表并计算其相似性得分。此外,现有的无监督图相似性学习方法主要基于聚类,它忽略了图对中体现的有价值的信息。为此,我们提出了一个对比度图匹配网络(CGMN),以进行自我监督的图形相似性学习,以计算任何两个输入图对象之间的相似性。具体而言,我们分别在一对中为每个图生成两个增强视图。然后,我们采用两种策略,即跨视图相互作用和跨刻画相互作用,以实现有效的节点表示学习。前者求助于两种观点中节点表示的一致性。后者用于识别不同图之间的节点差异。最后,我们通过汇总操作进行图形相似性计算将节点表示形式转换为图形表示。我们已经在八个现实世界数据集上评估了CGMN,实验结果表明,所提出的新方法优于图形相似性学习下游任务的最新方法。
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近年来,可微弱的建筑搜索(飞镖)已经受到了大量的关注,主要是因为它通过重量分享和连续放松来显着降低计算成本。然而,更近期的作品发现现有的可分辨率NAS技术难以俯视幼稚基线,产生劣化架构作为搜索所需。本文通过将体系结构权重放入高斯分布,而不是直接优化架构参数,而不是直接优化架构参数,而是作为分布学习问题。通过利用自然梯度变分推理(NGVI),可以基于现有的码票来容易地优化架构分布而不会产生更多内存和计算消耗。我们展示了贝叶斯原则的可分解NAS如何益处,提高勘探和提高稳定性。 NAS-BENCH-201和NAS-BENCH-1SHOT1基准数据集的实验结果证实了所提出的框架可以制造的重要改进。此外,我们还在学习参数上只需简单地应用argmax,我们进一步利用了NAS中最近提出的无培训代理,从优化分布中汲取的组架构中选择最佳架构,从而实现最终的架构-ART在NAS-BENCH-201和NAS-BENCH-1SHOT1基准上的结果。我们在飞镖搜索空间中的最佳架构也会分别获得2.37 \%,15.72 \%和24.2 \%的竞争性测试错误,分别在Cifar-10,CiFar-100和Imagenet数据集上。
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