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。
translated by 谷歌翻译
异质图卷积网络在解决异质网络数据的各种网络分析任务方面已广受欢迎,从链接预测到节点分类。但是,大多数现有作品都忽略了多型节点之间的多重网络的关系异质性,而在元路径中,元素嵌入中关系的重要性不同,这几乎无法捕获不同关系跨不同关系的异质结构信号。为了应对这一挑战,这项工作提出了用于异质网络嵌入的多重异质图卷积网络(MHGCN)。我们的MHGCN可以通过多层卷积聚合自动学习多重异质网络中不同长度的有用的异质元路径相互作用。此外,我们有效地将多相关结构信号和属性语义集成到学习的节点嵌入中,并具有无监督和精选的学习范式。在具有各种网络分析任务的五个现实世界数据集上进行的广泛实验表明,根据所有评估指标,MHGCN与最先进的嵌入基线的优势。
translated by 谷歌翻译
Inspired by the success of contrastive learning (CL) in computer vision and natural language processing, graph contrastive learning (GCL) has been developed to learn discriminative node representations on graph datasets. However, the development of GCL on Heterogeneous Information Networks (HINs) is still in the infant stage. For example, it is unclear how to augment the HINs without substantially altering the underlying semantics, and how to design the contrastive objective to fully capture the rich semantics. Moreover, early investigations demonstrate that CL suffers from sampling bias, whereas conventional debiasing techniques are empirically shown to be inadequate for GCL. How to mitigate the sampling bias for heterogeneous GCL is another important problem. To address the aforementioned challenges, we propose a novel Heterogeneous Graph Contrastive Multi-view Learning (HGCML) model. In particular, we use metapaths as the augmentation to generate multiple subgraphs as multi-views, and propose a contrastive objective to maximize the mutual information between any pairs of metapath-induced views. To alleviate the sampling bias, we further propose a positive sampling strategy to explicitly select positives for each node via jointly considering semantic and structural information preserved on each metapath view. Extensive experiments demonstrate HGCML consistently outperforms state-of-the-art baselines on five real-world benchmark datasets.
translated by 谷歌翻译
图表表示学习(GRL)对于图形结构数据分析至关重要。然而,大多数现有的图形神经网络(GNNS)严重依赖于标签信息,这通常是在现实世界中获得的昂贵。现有无监督的GRL方法遭受某些限制,例如对单调对比和可扩展性有限的沉重依赖。为了克服上述问题,鉴于最近的图表对比学习的进步,我们通过曲线图介绍了一种新颖的自我监控图形表示学习算法,即通过利用所提出的调整变焦方案来学习节点表示来学习节点表示。具体地,该机制使G-Zoom能够从多个尺度的图表中探索和提取自我监督信号:MICRO(即,节点级别),MESO(即,邻域级)和宏(即,子图级) 。首先,我们通过两个不同的图形增强生成输入图的两个增强视图。然后,我们逐渐地从节点,邻近逐渐为上述三个尺度建立三种不同的对比度,在那里我们最大限度地提高了横跨尺度的图形表示之间的协议。虽然我们可以从微距和宏观视角上从给定图中提取有价值的线索,但是邻域级对比度基于我们的调整后的缩放方案提供了可自定义选项的能力,以便手动选择位于微观和介于微观之间的最佳视点宏观透视更好地理解图数据。此外,为了使我们的模型可扩展到大图,我们采用了并行图形扩散方法来从图形尺寸下解耦模型训练。我们对现实世界数据集进行了广泛的实验,结果表明,我们所提出的模型始终始终优于最先进的方法。
translated by 谷歌翻译
关于图表的深度学习最近吸引了重要的兴趣。然而,大多数作品都侧重于(半)监督学习,导致缺点包括重标签依赖,普遍性差和弱势稳健性。为了解决这些问题,通过良好设计的借口任务在不依赖于手动标签的情况下提取信息知识的自我监督学习(SSL)已成为图形数据的有希望和趋势的学习范例。与计算机视觉和自然语言处理等其他域的SSL不同,图表上的SSL具有独家背景,设计理念和分类。在图表的伞下自我监督学习,我们对采用图表数据采用SSL技术的现有方法及时及全面的审查。我们构建一个统一的框架,数学上正式地规范图表SSL的范例。根据借口任务的目标,我们将这些方法分为四类:基于生成的,基于辅助性的,基于对比的和混合方法。我们进一步描述了曲线图SSL在各种研究领域的应用,并总结了绘图SSL的常用数据集,评估基准,性能比较和开源代码。最后,我们讨论了该研究领域的剩余挑战和潜在的未来方向。
translated by 谷歌翻译
Heterogeneous graph contrastive learning has received wide attention recently. Some existing methods use meta-paths, which are sequences of object types that capture semantic relationships between objects, to construct contrastive views. However, most of them ignore the rich meta-path context information that describes how two objects are connected by meta-paths. On the other hand, they fail to distinguish hard negatives from false negatives, which could adversely affect the model performance. To address the problems, we propose MEOW, a heterogeneous graph contrastive learning model that considers both meta-path contexts and weighted negative samples. Specifically, MEOW constructs a coarse view and a fine-grained view for contrast. The former reflects which objects are connected by meta-paths, while the latter uses meta-path contexts and characterizes the details on how the objects are connected. We take node embeddings in the coarse view as anchors, and construct positive and negative samples from the fine-grained view. Further, to distinguish hard negatives from false negatives, we learn weights of negative samples based on node clustering. We also use prototypical contrastive learning to pull close embeddings of nodes in the same cluster. Finally, we conduct extensive experiments to show the superiority of MEOW against other state-of-the-art methods.
translated by 谷歌翻译
Recently, contrastive learning (CL) has emerged as a successful method for unsupervised graph representation learning. Most graph CL methods first perform stochastic augmentation on the input graph to obtain two graph views and maximize the agreement of representations in the two views. Despite the prosperous development of graph CL methods, the design of graph augmentation schemes-a crucial component in CL-remains rarely explored. We argue that the data augmentation schemes should preserve intrinsic structures and attributes of graphs, which will force the model to learn representations that are insensitive to perturbation on unimportant nodes and edges. However, most existing methods adopt uniform data augmentation schemes, like uniformly dropping edges and uniformly shuffling features, leading to suboptimal performance. In this paper, we propose a novel graph contrastive representation learning method with adaptive augmentation that incorporates various priors for topological and semantic aspects of the graph. Specifically, on the topology level, we design augmentation schemes based on node centrality measures to highlight important connective structures. On the node attribute level, we corrupt node features by adding more noise to unimportant node features, to enforce the model to recognize underlying semantic information. We perform extensive experiments of node classification on a variety of real-world datasets. Experimental results demonstrate that our proposed method consistently outperforms existing state-of-the-art baselines and even surpasses some supervised counterparts, which validates the effectiveness of the proposed contrastive framework with adaptive augmentation. CCS CONCEPTS• Computing methodologies → Unsupervised learning; Neural networks; Learning latent representations.
translated by 谷歌翻译
尽管图表学习(GRL)取得了重大进展,但要以足够的方式提取和嵌入丰富的拓扑结构和特征信息仍然是一个挑战。大多数现有方法都集中在本地结构上,并且无法完全融合全球拓扑结构。为此,我们提出了一种新颖的结构保留图表学习(SPGRL)方法,以完全捕获图的结构信息。具体而言,为了减少原始图的不确定性和错误信息,我们通过k-nearest邻居方法构建了特征图作为互补视图。该特征图可用于对比节点级别以捕获本地关系。此外,我们通过最大化整个图形和特征嵌入的相互信息(MI)来保留全局拓扑结构信息,从理论上讲,该信息可以简化为交换功能的特征嵌入和原始图以重建本身。广泛的实验表明,我们的方法在半监督节点分类任务上具有相当出色的性能,并且在图形结构或节点特征上噪声扰动下的鲁棒性出色。
translated by 谷歌翻译
多视网网络嵌入旨在将网络中的节点投射到低维向量,同时保留其多重关系和属性信息。对比学习方法在这项任务中表现出了有希望的表现。但是,他们忽略了融合和视图表示之间的语义一致性,并且难以建模不同观点之间的互补信息。为了处理这些缺陷,这项工作为多视网网络嵌入(Creme)提供了一个新颖的对比学习框架。在我们的工作中,可以根据节点之间的各种关系来获得不同的观点。然后,我们通过适当的视图编码器生成视图嵌入,并利用细心的多视聚机来融合这些表示。特别是,我们设计了两个协作对比目标,即查看融合信息和视图中的信息,以自我监督的方式训练该模型。前目标将信息从不同观点产生的嵌入中提炼出来,而后者则捕获了观点之间的互补信息,以促进独特的视图嵌入。我们还表明,这两个目标可以统一为模型培训的一个目标。在三个现实世界数据集上进行的广泛实验表明,我们提出的奶油能够始终如一地超过最先进的方法。
translated by 谷歌翻译
We introduce a self-supervised approach for learning node and graph level representations by contrasting structural views of graphs. We show that unlike visual representation learning, increasing the number of views to more than two or contrasting multi-scale encodings do not improve performance, and the best performance is achieved by contrasting encodings from first-order neighbors and a graph diffusion. We achieve new state-ofthe-art results in self-supervised learning on 8 out of 8 node and graph classification benchmarks under the linear evaluation protocol. For example, on Cora (node) and Reddit-Binary (graph) classification benchmarks, we achieve 86.8% and 84.5% accuracy, which are 5.5% and 2.4% relative improvements over previous state-of-the-art. When compared to supervised baselines, our approach outperforms them in 4 out of 8 benchmarks.
translated by 谷歌翻译
最近,最大化的互信息是一种强大的无监测图表表示学习的方法。现有方法通常有效地从拓扑视图中捕获信息但忽略特征视图。为了规避这个问题,我们通过利用功能和拓扑视图利用互信息最大化提出了一种新的方法。具体地,我们首先利用多视图表示学习模块来更好地捕获跨图形上的特征和拓扑视图的本地和全局信息内容。为了模拟由特征和拓扑空间共享的信息,我们使用相互信息最大化和重建损耗最小化开发公共表示学习模块。要明确鼓励图形表示之间的多样性在相同的视图中,我们还引入了一个分歧正则化,以扩大同一视图之间的表示之间的距离。合成和实际数据集的实验证明了集成功能和拓扑视图的有效性。特别是,与先前的监督方法相比,我们所提出的方法可以在无监督的代表和线性评估协议下实现可比或甚至更好的性能。
translated by 谷歌翻译
In recent years, semi-supervised graph learning with data augmentation (DA) is currently the most commonly used and best-performing method to enhance model robustness in sparse scenarios with few labeled samples. Differing from homogeneous graph, DA in heterogeneous graph has greater challenges: heterogeneity of information requires DA strategies to effectively handle heterogeneous relations, which considers the information contribution of different types of neighbors and edges to the target nodes. Furthermore, over-squashing of information is caused by the negative curvature that formed by the non-uniformity distribution and strong clustering in complex graph. To address these challenges, this paper presents a novel method named Semi-Supervised Heterogeneous Graph Learning with Multi-level Data Augmentation (HG-MDA). For the problem of heterogeneity of information in DA, node and topology augmentation strategies are proposed for the characteristics of heterogeneous graph. And meta-relation-based attention is applied as one of the indexes for selecting augmented nodes and edges. For the problem of over-squashing of information, triangle based edge adding and removing are designed to alleviate the negative curvature and bring the gain of topology. Finally, the loss function consists of the cross-entropy loss for labeled data and the consistency regularization for unlabeled data. In order to effectively fuse the prediction results of various DA strategies, the sharpening is used. Existing experiments on public datasets, i.e., ACM, DBLP, OGB, and industry dataset MB show that HG-MDA outperforms current SOTA models. Additionly, HG-MDA is applied to user identification in internet finance scenarios, helping the business to add 30% key users, and increase loans and balances by 3.6%, 11.1%, and 9.8%.
translated by 谷歌翻译
用于异质图嵌入的图形神经网络是通过探索异质图的异质性和语义来将节点投射到低维空间中。但是,一方面,大多数现有的异质图嵌入方法要么不足以对特定语义下的局部结构进行建模,要么在汇总信息时忽略异质性。另一方面,来自多种语义的表示形式未全面整合以获得多功能节点嵌入。为了解决该问题,我们通过引入多视图表示学习的概念,提出了一个具有多视图表示学习(名为MV-HETGNN)的异质图神经网络(称为MV-HETGNN)。所提出的模型由节点特征转换,特定于视图的自我图编码和自动多视图融合,以彻底学习复杂的结构和语义信息,以生成全面的节点表示。在三个现实世界的异质图数据集上进行的广泛实验表明,所提出的MV-HETGNN模型始终优于各种下游任务中所有最新的GNN基准,例如节点分类,节点群集和链接预测。
translated by 谷歌翻译
图是对物体之间关系的强大表示,吸引了很多关注。图形学习的一个基本挑战是如何在没有标签的情况下训练有效的图形神经网络(GNN)编码器,这些标签既昂贵又耗时。对比学习(CL)是应对这一挑战的最受欢迎的范式之一,该挑战通过区分正和负节点对来训练GNN。尽管最近的CL方法取得了成功,但仍然存在两个爆炸案。首先,如何减少基于随机拓扑的数据增强引入的语义错误。传统CL通过节点级拓扑接近定义正和负节点对,该节点拓扑接近度仅基于图形拓扑,而不论节点属性的语义信息如何,因此某些语义上相似的节点可能被错误地视为负对。其次,如何有效地对现实图形的多重性进行建模,其中节点通过各种关系连接,并且每个关系都可以形成均匀的图层。为了解决这些问题,我们提出了一种新型的多重异质图原型对比度倾斜(X-GAL)框架来提取节点嵌入。 X-GOAL由两个组成部分组成:目标框架,该目标框架学习每个均匀图层的节点嵌入,以及一个对齐正则化,通过对齐层特定的节点嵌入来共同对不同的层进行模拟不同的层。具体而言,目标框架通过简洁的图形转换技术捕获节点级信息,并通过将节点拉到嵌入空间中的同一语义簇中,从而捕获群集级信息。对齐正则化在节点和群集级别的层上对齐嵌入。我们在各种现实世界数据集和下游任务上评估X-GAL,以证明其有效性。
translated by 谷歌翻译
无监督的图形表示学习是图形数据的非琐碎主题。在结构化数据的无监督代表学习中对比学习和自我监督学习的成功激发了图表上的类似尝试。使用对比损耗的当前无监督的图形表示学习和预培训主要基于手工增强图数据之间的对比度。但是,由于不可预测的不变性,图数据增强仍然没有很好地探索。在本文中,我们提出了一种新颖的协作图形神经网络对比学习框架(CGCL),它使用多个图形编码器来观察图形。不同视图观察的特征充当了图形编码器之间对比学习的图表增强,避免了任何扰动以保证不变性。 CGCL能够处理图形级和节点级表示学习。广泛的实验表明CGCL在无监督的图表表示学习中的优势以及图形表示学习的手工数据增强组合的非必要性。
translated by 谷歌翻译
Graph neural network, as a powerful graph representation technique based on deep learning, has shown superior performance and attracted considerable research interest. However, it has not been fully considered in graph neural network for heterogeneous graph which contains different types of nodes and links. The heterogeneity and rich semantic information bring great challenges for designing a graph neural network for heterogeneous graph. Recently, one of the most exciting advancements in deep learning is the attention mechanism, whose great potential has been well demonstrated in various areas. In this paper, we first propose a novel heterogeneous graph neural network based on the hierarchical attention, including node-level and semantic-level attentions. Specifically, the node-level attention aims to learn the importance between a node and its metapath based neighbors, while the semantic-level attention is able to learn the importance of different meta-paths. With the learned importance from both node-level and semantic-level attention, the importance of node and meta-path can be fully considered. Then the proposed model can generate node embedding by aggregating features from meta-path based neighbors in a hierarchical manner. Extensive experimental results on three real-world heterogeneous graphs not only show the superior performance of our proposed model over the state-of-the-arts, but also demonstrate its potentially good interpretability for graph analysis.
translated by 谷歌翻译
对比学习在图表学习领域表现出了巨大的希望。通过手动构建正/负样本,大多数图对比度学习方法依赖于基于矢量内部产品的相似性度量标准来区分图形表示样品。但是,手工制作的样品构建(例如,图表的节点或边缘的扰动)可能无法有效捕获图形的固有局部结构。同样,基于矢量内部产品的相似性度量标准无法完全利用图形的局部结构来表征图差。为此,在本文中,我们提出了一种基于自适应子图生成的新型对比度学习框架,以实现有效且强大的自我监督图表示学习,并且最佳传输距离被用作子绘图之间的相似性度量。它的目的是通过捕获图的固有结构来生成对比样品,并根据子图的特征和结构同时区分样品。具体而言,对于每个中心节点,通过自适应学习关系权重与相应邻域的节点,我们首先开发一个网络来生成插值子图。然后,我们分别构建来自相同和不同节点的子图的正和负对。最后,我们采用两种类型的最佳运输距离(即Wasserstein距离和Gromov-Wasserstein距离)来构建结构化的对比损失。基准数据集上的广泛节点分类实验验证了我们的图形对比学习方法的有效性。
translated by 谷歌翻译
注意机制使图形神经网络(GNN)能够学习目标节点与其单跳邻居之间的注意力权重,从而进一步提高性能。但是,大多数现有的GNN都针对均匀图,其中每一层只能汇总单跳邻居的信息。堆叠多层网络引入了相当大的噪音,并且很容易导致过度平滑。我们在这里提出了一种多跃波异质邻域信息融合图表示方法(MHNF)。具体而言,我们提出了一个混合元自动提取模型,以有效提取多ihop混合邻居。然后,我们制定了一个跳级的异质信息聚合模型,该模型在同一混合Metapath中选择性地汇总了不同的跳跃邻域信息。最后,构建了分层语义注意融合模型(HSAF),该模型可以有效地整合不同的互动和不同的路径邻域信息。以这种方式,本文解决了汇总MultiHop邻里信息和学习目标任务的混合元数据的问题。这减轻了手动指定Metapaths的限制。此外,HSAF可以提取Metapaths的内部节点信息,并更好地整合存在不同级别的语义信息。真实数据集的实验结果表明,MHNF在最先进的基准中取得了最佳或竞争性能,仅1/10〜1/100参数和计算预算。我们的代码可在https://github.com/phd-lanyu/mhnf上公开获取。
translated by 谷歌翻译
许多真实世界图(网络)是具有不同类型的节点和边缘的异构。异构图嵌入,旨在学习异构图的低维节点表示,对于各种下游应用至关重要。已经提出了许多基于元路径的嵌入方法来学习近年来异构图的语义信息。然而,在学习异构图形嵌入时,大多数现有技术都在图形结构信息中忽略了图形结构信息。本文提出了一种新颖的结构意识异构图形神经网络(SHGNN),以解决上述限制。详细地,我们首先利用特征传播模块来捕获元路径中中间节点的本地结构信息。接下来,我们使用树关注聚合器将图形结构信息结合到元路径上的聚合模块中。最后,我们利用了元路径聚合器熔断来自不同元路径的聚合的信息。我们对节点分类和聚类任务进行了实验,并在基准数据集中实现了最先进的结果,该数据集显示了我们所提出的方法的有效性。
translated by 谷歌翻译
由于学术和工业领域的异质图无处不在,研究人员最近提出了许多异质图神经网络(HGNN)。在本文中,我们不再采用更强大的HGNN模型,而是有兴趣设计一个多功能的插件模块,该模块解释了从预先训练的HGNN中提取的关系知识。据我们所知,我们是第一个在异质图上提出高阶(雇用)知识蒸馏框架的人,无论HGNN的模型体系结构如何,它都可以显着提高预测性能。具体而言,我们的雇用框架最初执行一阶节点级知识蒸馏,该蒸馏曲线及其预测逻辑编码了老师HGNN的语义。同时,二阶关系级知识蒸馏模仿了教师HGNN生成的不同类型的节点嵌入之间的关系相关性。在各种流行的HGNN模型和三个现实世界的异质图上进行了广泛的实验表明,我们的方法获得了一致且相当大的性能增强,证明了其有效性和泛化能力。
translated by 谷歌翻译