在本文中，我们提出了联合关注（CAS），一类新颖的学习与参加图形神经网络（GNN）的策略。除了考虑GNN内传播的层面节点特征，CAS可以另外包含各种结构干预，例如节点集群嵌入，以及在计算注意力分数时可以在GNN之外学习的高阶结构相关性。因此，由联合标准视为重要的节点特征，因此更可能在GNN中传播。鉴于新颖的联合注意力策略，我们提出了可以学习嵌入嵌入的表示的表演的图表关注网络（CAT），这些网络嵌入具有与联合关注所认为的显着潜在的特征。此外，我们理论上验证了猫的辨别能力。利用所提出的联合注意力策略的猫已经在建立了完善的基准数据集中广泛测试，并与最先进的基线进行了全面的基础测试。所获得的显着性能证明了所提出的联合关注的有效性。

图表可以模拟实体之间的复杂交互，它在许多重要的应用程序中自然出现。这些应用程序通常可以投入到标准图形学习任务中，其中关键步骤是学习低维图表示。图形神经网络（GNN）目前是嵌入方法中最受欢迎的模型。然而，邻域聚合范例中的标准GNN患有区分\ EMPH {高阶}图形结构的有限辨别力，而不是\ EMPH {低位}结构。为了捕获高阶结构，研究人员求助于主题和开发的基于主题的GNN。然而，现有的基于主基的GNN仍然仍然遭受较少的辨别力的高阶结构。为了克服上述局限性，我们提出了一个新颖的框架，以更好地捕获高阶结构的新框架，铰接于我们所提出的主题冗余最小化操作员和注射主题组合的新颖框架。首先，MGNN生成一组节点表示W.R.T.每个主题。下一阶段是我们在图案中提出的冗余最小化，该主题在彼此相互比较并蒸馏出每个主题的特征。最后，MGNN通过组合来自不同图案的多个表示来执行节点表示的更新。特别地，为了增强鉴别的功率，MGNN利用重新注射功能来组合表示的函数w.r.t.不同的主题。我们进一步表明，我们的拟议体系结构增加了GNN的表现力，具有理论分析。我们展示了MGNN在节点分类和图形分类任务上的七个公共基准上表现出最先进的方法。

异质图具有多个节点和边缘类型，并且在语义上比同质图更丰富。为了学习这种复杂的语义，许多用于异质图的图形神经网络方法使用Metapaths捕获节点之间的多跳相互作用。通常，非目标节点的功能未纳入学习过程。但是，可以存在涉及多个节点或边缘的非线性高阶相互作用。在本文中，我们提出了Simplicial Graph注意网络（SGAT），这是一种简单的复杂方法，可以通过将非目标节点的特征放在简单上来表示这种高阶相互作用。然后，我们使用注意机制和上邻接来生成表示。我们凭经验证明了方法在异质图数据集上使用节点分类任务的方法的功效，并进一步显示了SGAT通过采用随机节点特征来提取结构信息的能力。数值实验表明，SGAT的性能优于其他当前最新的异质图学习方法。

We investigate the representation power of graph neural networks in the semisupervised node classification task under heterophily or low homophily, i.e., in networks where connected nodes may have different class labels and dissimilar features. Many popular GNNs fail to generalize to this setting, and are even outperformed by models that ignore the graph structure (e.g., multilayer perceptrons). Motivated by this limitation, we identify a set of key designs-ego-and neighbor-embedding separation, higher-order neighborhoods, and combination of intermediate representations-that boost learning from the graph structure under heterophily. We combine them into a graph neural network, H 2 GCN, which we use as the base method to empirically evaluate the effectiveness of the identified designs. Going beyond the traditional benchmarks with strong homophily, our empirical analysis shows that the identified designs increase the accuracy of GNNs by up to 40% and 27% over models without them on synthetic and real networks with heterophily, respectively, and yield competitive performance under homophily.

We present graph attention networks (GATs), novel neural network architectures that operate on graph-structured data, leveraging masked self-attentional layers to address the shortcomings of prior methods based on graph convolutions or their approximations. By stacking layers in which nodes are able to attend over their neighborhoods' features, we enable (implicitly) specifying different weights to different nodes in a neighborhood, without requiring any kind of costly matrix operation (such as inversion) or depending on knowing the graph structure upfront. In this way, we address several key challenges of spectral-based graph neural networks simultaneously, and make our model readily applicable to inductive as well as transductive problems. Our GAT models have achieved or matched state-of-theart results across four established transductive and inductive graph benchmarks: the Cora, Citeseer and Pubmed citation network datasets, as well as a proteinprotein interaction dataset (wherein test graphs remain unseen during training).

Graph Neural Networks (GNNs) are an effective framework for representation learning of graphs. GNNs follow a neighborhood aggregation scheme, where the representation vector of a node is computed by recursively aggregating and transforming representation vectors of its neighboring nodes. Many GNN variants have been proposed and have achieved state-of-the-art results on both node and graph classification tasks. However, despite GNNs revolutionizing graph representation learning, there is limited understanding of their representational properties and limitations. Here, we present a theoretical framework for analyzing the expressive power of GNNs to capture different graph structures. Our results characterize the discriminative power of popular GNN variants, such as Graph Convolutional Networks and GraphSAGE, and show that they cannot learn to distinguish certain simple graph structures. We then develop a simple architecture that is provably the most expressive among the class of GNNs and is as powerful as the Weisfeiler-Lehman graph isomorphism test. We empirically validate our theoretical findings on a number of graph classification benchmarks, and demonstrate that our model achieves state-of-the-art performance. * Equal contribution. † Work partially performed while in Tokyo, visiting Prof. Ken-ichi Kawarabayashi.

由于图神经网络（GNN）的成功和异质信息网络的广泛应用，近年来，异质图学习近年来引起了极大的关注。已经提出了各种异质图神经网络，以概括GNN来处理异质图。不幸的是，这些方法通过各种复杂的模块对异质性进行建模。本文旨在提出一个简单而有效的框架，以使均质GNN具有足够的处理异质图的能力。具体而言，我们提出了基于关系嵌入的图形神经网络（RE-GNNS），该图形仅使用一个参数来嵌入边缘类型关系和自动连接的重要性。为了同时优化这些关系嵌入和其他参数，提出了一个梯度缩放因子来约束嵌入以收敛到合适的值。此外，我们从理论上证明，与基于元路径的异质GNN相比，我们的RE-GNN具有更高的表现力。关于节点分类任务的广泛实验验证了我们提出的方法的有效性。

Graph Neural Networks (GNNs) have attracted increasing attention in recent years and have achieved excellent performance in semi-supervised node classification tasks. The success of most GNNs relies on one fundamental assumption, i.e., the original graph structure data is available. However, recent studies have shown that GNNs are vulnerable to the complex underlying structure of the graph, making it necessary to learn comprehensive and robust graph structures for downstream tasks, rather than relying only on the raw graph structure. In light of this, we seek to learn optimal graph structures for downstream tasks and propose a novel framework for semi-supervised classification. Specifically, based on the structural context information of graph and node representations, we encode the complex interactions in semantics and generate semantic graphs to preserve the global structure. Moreover, we develop a novel multi-measure attention layer to optimize the similarity rather than prescribing it a priori, so that the similarity can be adaptively evaluated by integrating measures. These graphs are fused and optimized together with GNN towards semi-supervised classification objective. Extensive experiments and ablation studies on six real-world datasets clearly demonstrate the effectiveness of our proposed model and the contribution of each component.

在过去几年中，人们对代表性学习的图形神经网络（GNN）的兴趣不大。GNN提供了一个一般有效的框架，可以从图形结构化数据中学习。但是，GNN通常仅使用一个非常有限的邻域的信息来避免过度光滑。希望为模型提供更多信息。在这项工作中，我们将个性化Pagerank（PPR）的极限分布纳入图形注意力网络（GATS）中，以反映较大的邻居信息，而无需引入过度光滑。从直觉上讲，基于个性化Pagerank的消息聚合对应于无限的许多邻里聚合层。我们表明，对于四个广泛使用的基准数据集，我们的模型优于各种基线模型。我们的实施已在线公开。

几何深度学习取得了长足的进步，旨在概括从传统领域到非欧几里得群岛的结构感知神经网络的设计，从而引起图形神经网络（GNN），这些神经网络（GNN）可以应用于形成的图形结构数据，例如社会，例如，网络，生物化学和材料科学。尤其是受欧几里得对应物的启发，尤其是图形卷积网络（GCN）通过提取结构感知功能来成功处理图形数据。但是，当前的GNN模型通常受到各种现象的限制，这些现象限制了其表达能力和推广到更复杂的图形数据集的能力。大多数模型基本上依赖于通过本地平均操作对图形信号的低通滤波，从而导致过度平滑。此外，为了避免严重的过度厚度，大多数流行的GCN式网络往往是较浅的，并且具有狭窄的接收场，导致侵犯。在这里，我们提出了一个混合GNN框架，该框架将传统的GCN过滤器与通过几何散射定义的带通滤波器相结合。我们进一步介绍了一个注意框架，该框架允许该模型在节点级别上从不同过滤器的组合信息进行本地参与。我们的理论结果确定了散射过滤器的互补益处，以利用图表中的结构信息，而我们的实验显示了我们方法对各种学习任务的好处。

最近关于图表卷积网络（GCN）的研究表明，初始节点表示（即，第一次图卷积前的节点表示）很大程度上影响最终的模型性能。但是，在学习节点的初始表示时，大多数现有工作线性地组合了节点特征的嵌入，而不考虑特征之间的交互（或特征嵌入）。我们认为，当节点特征是分类时，例如，在许多实际应用程序中，如用户分析和推荐系统，功能交互通常会对预测分析进行重要信号。忽略它们将导致次优初始节点表示，从而削弱后续图表卷积的有效性。在本文中，我们提出了一个名为CatGCN的新GCN模型，当节点功能是分类时，为图表学习量身定制。具体地，我们将显式交互建模的两种方式集成到初始节点表示的学习中，即在每对节点特征上的本地交互建模和人工特征图上的全局交互建模。然后，我们通过基于邻域聚合的图形卷积来优化增强的初始节点表示。我们以端到端的方式训练CatGCN，并在半监督节点分类上展示它。来自腾讯和阿里巴巴数据集的三个用户分析的三个任务（预测用户年龄，城市和购买级别）的大量实验验证了CatGCN的有效性，尤其是在图表卷积之前执行特征交互建模的积极效果。

我们专注于使用图形神经网络（GNN）模型来分类的图形分类，该模型预先计算了使用并行排列的邻域聚合图操作员的Bank的节点功能。这些GNN模型具有降低培训和推理时间，由于预兆，而且还与流行的GNN变体不同，这些VNN变体通过训练期间通过顺序邻域聚合过程更新节点特征。我们提供了理论条件，其中具有平行邻域聚集（简称PA-GNN的PA-GNN）的通用GNN模型作为鉴别非同胞图的众所周知的Weisfeiler-Lehman（WL）曲线构同试验。虽然PA-GNN模型与WL测试没有明显的关系，但我们表明从这两种方法获得的图形嵌入是无标有关的。然后，我们提出了一个专门的PA-GNN模型，称为旋转，从而携带开发的条件。我们通过数值实验证明了开发的模型在许多不同的现实世界数据集上实现了最先进的性能，同时保持WL测试的辨别力和训练过程之前预处理图的计算优势。

Deep learning has revolutionized many machine learning tasks in recent years, ranging from image classification and video processing to speech recognition and natural language understanding. The data in these tasks are typically represented in the Euclidean space. However, there is an increasing number of applications where data are generated from non-Euclidean domains and are represented as graphs with complex relationships and interdependency between objects. The complexity of graph data has imposed significant challenges on existing machine learning algorithms. Recently, many studies on extending deep learning approaches for graph data have emerged. In this survey, we provide a comprehensive overview of graph neural networks (GNNs) in data mining and machine learning fields. We propose a new taxonomy to divide the state-of-the-art graph neural networks into four categories, namely recurrent graph neural networks, convolutional graph neural networks, graph autoencoders, and spatial-temporal graph neural networks. We further discuss the applications of graph neural networks across various domains and summarize the open source codes, benchmark data sets, and model evaluation of graph neural networks. Finally, we propose potential research directions in this rapidly growing field.

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。

Graph neural networks (GNNs) have been widely used under semi-supervised settings. Prior studies have mainly focused on finding appropriate graph filters (e.g., aggregation schemes) to generalize well for both homophilic and heterophilic graphs. Even though these approaches are essential and effective, they still suffer from the sparsity in initial node features inherent in the bag-of-words representation. Common in semi-supervised learning where the training samples often fail to cover the entire dimensions of graph filters (hyperplanes), this can precipitate over-fitting of specific dimensions in the first projection matrix. To deal with this problem, we suggest a simple and novel strategy; create additional space by flipping the initial features and hyperplane simultaneously. Training in both the original and in the flip space can provide precise updates of learnable parameters. To the best of our knowledge, this is the first attempt that effectively moderates the overfitting problem in GNN. Extensive experiments on real-world datasets demonstrate that the proposed technique improves the node classification accuracy up to 40.2 %

In the last few years, graph neural networks (GNNs) have become the standard toolkit for analyzing and learning from data on graphs. This emerging field has witnessed an extensive growth of promising techniques that have been applied with success to computer science, mathematics, biology, physics and chemistry. But for any successful field to become mainstream and reliable, benchmarks must be developed to quantify progress. This led us in March 2020 to release a benchmark framework that i) comprises of a diverse collection of mathematical and real-world graphs, ii) enables fair model comparison with the same parameter budget to identify key architectures, iii) has an open-source, easy-to-use and reproducible code infrastructure, and iv) is flexible for researchers to experiment with new theoretical ideas. As of December 2022, the GitHub repository has reached 2,000 stars and 380 forks, which demonstrates the utility of the proposed open-source framework through the wide usage by the GNN community. In this paper, we present an updated version of our benchmark with a concise presentation of the aforementioned framework characteristics, an additional medium-sized molecular dataset AQSOL, similar to the popular ZINC, but with a real-world measured chemical target, and discuss how this framework can be leveraged to explore new GNN designs and insights. As a proof of value of our benchmark, we study the case of graph positional encoding (PE) in GNNs, which was introduced with this benchmark and has since spurred interest of exploring more powerful PE for Transformers and GNNs in a robust experimental setting.

消息传递已作为设计图形神经网络（GNN）的有效工具的发展。但是，消息传递的大多数现有方法简单地简单或平均所有相邻的功能更新节点表示。它们受到两个问题的限制，即（i）缺乏可解释性来识别对GNN的预测重要的节点特征，以及（ii）特征过度混合，导致捕获长期依赖和无能为力的过度平滑问题在异质或低同质的下方处理图。在本文中，我们提出了一个节点级胶囊图神经网络（NCGNN），以通过改进的消息传递方案来解决这些问题。具体而言，NCGNN表示节点为节点级胶囊组，其中每个胶囊都提取其相应节点的独特特征。对于每个节点级胶囊，开发了一个新颖的动态路由过程，以适应适当的胶囊，以从设计的图形滤波器确定的子图中聚集。 NCGNN聚集仅有利的胶囊并限制无关的消息，以避免交互节点的过度混合特征。因此，它可以缓解过度平滑的问题，并通过同粒或异质的图表学习有效的节点表示。此外，我们提出的消息传递方案本质上是可解释的，并免于复杂的事后解释，因为图形过滤器和动态路由过程确定了节点特征的子集，这对于从提取的子分类中的模型预测最为重要。关于合成和现实图形的广泛实验表明，NCGNN可以很好地解决过度光滑的问题，并为半监视的节点分类产生更好的节点表示。它的表现优于同质和异质的艺术状态。

图形神经网络已成为从图形结构数据学习的不可缺少的工具之一，并且它们的实用性已在各种各样的任务中显示。近年来，建筑设计的巨大改进，导致各种预测任务的性能更好。通常，这些神经架构在同一层中使用可知的权重矩阵组合节点特征聚合和特征转换。这使得分析从各种跳过的节点特征和神经网络层的富有效力来挑战。由于不同的图形数据集显示在特征和类标签分布中的不同级别和异常级别，因此必须了解哪些特征对于没有任何先前信息的预测任务是重要的。在这项工作中，我们将节点特征聚合步骤和深度与图形神经网络分离，并经验分析了不同的聚合特征在预测性能中发挥作用。我们表明，并非通过聚合步骤生成的所有功能都很有用，并且通常使用这些较少的信息特征可能对GNN模型的性能有害。通过我们的实验，我们表明学习这些功能的某些子集可能会导致各种数据集的性能更好。我们建议使用Softmax作为常规器，并从不同跳距的邻居聚合的功能的“软选择器”;和L2 - GNN层的标准化。结合这些技术，我们呈现了一个简单浅的模型，特征选择图神经网络（FSGNN），并经验展示所提出的模型比九个基准数据集中的最先进的GNN模型实现了可比或甚至更高的准确性节点分类任务，具有显着的改进，可达51.1％。

图形神经网络（GNN）在解决图形结构数据（即网络）方面的各种分析任务方面已广受欢迎。典型的gnns及其变体遵循一种消息的方式，该方式通过网络拓扑沿网络拓扑的特征传播过程获得网络表示，然而，它们忽略了许多现实世界网络中存在的丰富文本语义（例如，局部单词序列）。现有的文本丰富网络方法通过主要利用内部信息（例如主题或短语/单词）来整合文本语义，这些信息通常无法全面地挖掘文本语义，从而限制了网络结构和文本语义之间的相互指导。为了解决这些问题，我们提出了一个具有外部知识（TEKO）的新型文本富裕的图形神经网络，以充分利用文本丰富的网络中的结构和文本信息。具体而言，我们首先提出一个灵活的异质语义网络，该网络结合了文档和实体之间的高质量实体和互动。然后，我们介绍两种类型的外部知识，即结构化的三胞胎和非结构化实体描述，以更深入地了解文本语义。我们进一步为构建的异质语义网络设计了互惠卷积机制，使网络结构和文本语义能够相互协作并学习高级网络表示。在四个公共文本丰富的网络以及一个大规模的电子商务搜索数据集上进行了广泛的实验结果，这说明了Teko优于最先进的基线。

Deep learning has been shown to be successful in a number of domains, ranging from acoustics, images, to natural language processing. However, applying deep learning to the ubiquitous graph data is non-trivial because of the unique characteristics of graphs. Recently, substantial research efforts have been devoted to applying deep learning methods to graphs, resulting in beneficial advances in graph analysis techniques. In this survey, we comprehensively review the different types of deep learning methods on graphs. We divide the existing methods into five categories based on their model architectures and training strategies: graph recurrent neural networks, graph convolutional networks, graph autoencoders, graph reinforcement learning, and graph adversarial methods. We then provide a comprehensive overview of these methods in a systematic manner mainly by following their development history. We also analyze the differences and compositions of different methods. Finally, we briefly outline the applications in which they have been used and discuss potential future research directions.