目录
4. KST-GCN：用于流量预测的知识驱动的时空图卷积网络
6.STGC-GNNs：基于 GNN 的交通预测框架，具有时空格兰杰因果关系图
8. 用于动态图的高阶拓扑增强图卷积网络 （HoT-GCN）
10. LSTTN：用于交通流量预测的基于长短期 transformer 的时空神经网络
代码地址：网址
1. T-GCN：用于流量预测的时间图卷积网络
Accurate and real-time traffic forecasting plays an important role in the Intelligent Traffic System and is of great significance for urban traffic planning, traffic management, and traffic control. However, traffic forecasting has always been considered an open scientific issue, owing to the constraints of urban road network topological structure and the law of dynamic change with time, namely, spatial dependence and temporal dependence. To capture the spatial and temporal dependence simultaneously, we propose a novel neural network-based traffic forecasting method, the temporal graph convolutional network (T-GCN) model, which is in combination with the graph convolutional network (GCN) and gated recurrent unit (GRU). Specifically, the GCN is used to learn complex topological structures to capture spatial dependence and the gated recurrent unit is used to learn dynamic changes of traffic data to capture temporal dependence. Then, the T-GCN model is employed to traffic forecasting based on the urban road network. Experiments demonstrate that our T-GCN model can obtain the spatio-temporal correlation from traffic data and the predictions outperform state-of-art baselines on real-world traffic datasets.
准确、实时的交通预测在智能交通系统中发挥着重要作用，对城市交通规划、交通管理和交通控制具有重要意义。然而，由于城市路网拓扑结构的约束和随时间动态变化的规律，即空间依赖性和时间依赖通预测一直被认为是一个开放的科学问题。为了同时捕捉空间和时间依赖性，我们提出了一种新的基于神经网络的交通预测方法，即时间图卷积网络 （T-GCN） 模型，它与图卷积网络 （GCN） 和门控循环单元 （GRU） 相结合。具体来说，GCN 用于学习复杂的拓扑结构以捕获空间依赖性，而门控循环单元用于学习交通数据的动态变化以捕获时间依赖性。然后，采用 T-GCN 模型进行基于城市路网的交通预测;实验表明，我们的 T-GCN 模型可以从交通数据中获得时空相关性，并且预测结果优于真实世界交通数据集上最先进的基线。
2. A3T-GCN： 用于流量预测的注意力时间图卷积网络
Accurate real-time traffic forecasting is a core technological problem against the implementation of the intelligent transportation system. However, it remains challenging considering the complex spatial and temporal dependencies among traffic flows. In the spatial dimension, due to the connectivity of the road network, the traffic flows between linked roads are closely related. In terms of the temporal factor, although there exists a tendency among adjacent time points in general, the importance of distant past points is not necessarily smaller than that of recent past points since traffic flows are also affected by external factors. In this study, an attention temporal graph convolutional network (A3T-GCN) traffic forecasting method was proposed to simultaneously capture global temporal dynamics and spatial correlations. The A3T-GCN model learns the short-time trend in time series by using the gated recurrent units and learns the spatial dependence based on the topology of the road network through the graph convolutional network. Moreover, the attention mechanism was introduced to adjust the importance of different time points and assemble global temporal information to improve prediction accuracy. Experimental results in real-world datasets demonstrate the effectiveness and robustness of proposed A3T-GCN.
精准的实时交通预测是反对智能交通系统实施的核心技术问题。然而，考虑到交通流之间复杂的空间和时间依赖关系，这仍然具有挑战性。在空间维度上，由于道路网络的连通性，相连道路之间的交通流密切相关。从时间因素来看，虽然相邻时间点之间总体上存在趋势，但由于交通流量也受到外部因素的影响，远距离过去点的重要性不一定小于近期过去点的重要性。本研究提出了一种注意力时间图卷积网络 （A3T-GCN） 流量预测方法，以同时捕获全局时间动态和空间相关性。A3T-GCN 模型利用门控循环单元学习时间序列中的短时趋势，并通过图卷积网络基于道路网络的拓扑学习空间依赖性。此外，引入注意力机制来调整不同时间点的重要性并组装全局时间信息，以提高预测准确性。真实世界数据集中的实验结果证明了所提出的 A3T-GCN 的有效性和稳健性。
3. AST-GCN：用于流量预测的属性增强时空图卷积网络
Traffic forecasting is a fundamental and challenging task in the field of intelligent transportation. Accurate forecasting not only depends on the historical traffic flow information but also needs to consider the influence of a variety of external factors, such as weather conditions and surrounding POI distribution. Recently, spatiotemporal models integrating graph convolutional networks and recurrent neural networks have become traffic forecasting research hotspots and have made significant progress. However, few works integrate external factors. Therefore, based on the assumption that introducing external factors can enhance the spatiotemporal accuracy in predicting traffic and improving interpretability, we propose an attribute-augmented spatiotemporal graph convolutional network (AST-GCN). We model the external factors as dynamic attributes and static attributes and design an attribute-augmented unit to encode and integrate those factors into the spatiotemporal graph convolution model. Experiments on real datasets show the effectiveness of considering external information on traffic speed forecasting tasks when compared with traditional traffic prediction methods. Moreover, under different attribute-augmented schemes and prediction horizon settings, the forecasting accuracy of the AST-GCN is higher than that of the baselines.
交通预测是智能交通领域一项基础性且具有挑战性的任务。准确的预测不仅取决于历史交通流量信息，还需要考虑多种外部因素的影响，例如天气状况和周围的 POI 分布。近年来，融合图卷积网络和递归神经网络的时空模型成为交通预测研究热点，并取得了重大进展。然而，很少有作品融合了外部因素。因此，基于引入外部因素可以提高预测流量的时空准确性和提高可解释性的假设，我们提出了一种属性增强的时空图卷积网络 （AST-GCN）。我们将外部因素建模为动态属性和静态属性，并设计一个属性增强单元来编码这些因素并将其集成到时空图卷积模型中。在真实数据集上的实验表明，与传统的交通预测方法相比，在交通速度预测任务中考虑外部信息的有效性。此外，在不同的属性增强方案和预测范围设置下，AST-GCN 的预测精度高于基线。
4. KST-GCN：用于流量预测的知识驱动的时空图卷积网络
While considering the spatial and temporal features of traffic, capturing the impacts of various external factors on travel is an essential step towards achieving accurate traffic forecasting. However, existing studies seldom consider external factors or neglect the effect of the complex correlations among external factors on traffic. Intuitively, knowledge graphs can naturally describe these correlations. Since knowledge graphs and traffic networks are essentially heterogeneous networks, it is challenging to integrate the information in both networks. On this background, this study presents a knowledge representation-driven traffic forecasting method based on spatial-temporal graph convolutional networks. We first construct a knowledge graph for traffic forecasting and derive knowledge representations by a knowledge representation learning method named KR-EAR. Then, we propose the Knowledge Fusion Cell (KF-Cell) to combine the knowledge and traffic features as the input of a spatial-temporal graph convolutional backbone network. Experimental results on the real-world dataset show that our strategy enhances the forecasting performances of backbones at various prediction horizons. The ablation and perturbation analysis further verify the effectiveness and robustness of the proposed method. To the best of our knowledge, this is the first study that constructs and utilizes a knowledge graph to facilitate traffic forecasting; it also offers a promising direction to integrate external information and spatial-temporal information for traffic forecasting.
在考虑交通的空间和时间特征时，捕获各种外部因素对出行的影响是实现准确交通预测的重要步骤。然而，现有的研究很少考虑外部因素或忽视外部因素之间的复杂相关性对交通的影响。直观地说，知识图谱可以自然地描述这些相关性。由于知识图谱和流量网络本质上是异构网络，因此在这两个网络中集成信息具有挑战性。在此背景下，本研究提出了一种基于时空图卷积网络的知识表示驱动的交通预测方法。我们首先构建一个用于流量预测的知识图谱，并通过一个名为 KR-EAR 的知识表示学习方法推导出知识表示。然后，我们提出了知识融合单元 （KF-Cell），将知识和交通特征相结合，作为时空图卷积骨干网络的输入。在真实数据集上的实验结果表明，我们的策略增强了 backbone 在各种预测范围内的预测性能。消融和扰动分析进一步验证了所提方法的有效性和稳健性。据我们所知，这是第一项构建和利用知识图谱来促进流量预测的研究;它还为整合外部信息和时空信息进行交通预测提供了一个有前途的方向。
5. 曲率图神经网络
Graph neural networks (GNNs) have achieved great success in many graph-based tasks. Much work is dedicated to empowering GNNs with adaptive locality ability, which enables the measurement of the importance of neighboring nodes to the target node by a node-specific mechanism. However, the current node-specific mechanisms are deficient in distinguishing the importance of nodes in the topology structure. We believe that the structural importance of neighboring nodes is closely related to their importance in aggregation. In this paper, we introduce discrete graph curvature (the Ricci curvature) to quantify the strength of the structural connection of pairwise nodes. We propose a curvature graph neural network (CGNN), which effectively improves the adaptive locality ability of GNNs by leveraging the structural properties of graph curvature. To improve the adaptability of curvature on various datasets, we explicitly transform curvature into the weights of neighboring nodes by the necessary negative curvature processing module and curvature normalization module. Then, we conduct numerous experiments on various synthetic and real-world datasets. The experimental results on synthetic datasets show that CGNN effectively exploits the topology structure information and that the performance is significantly improved. CGNN outperforms the baselines on 5 dense node classification benchmark datasets. This study provides a deepened understanding of how to utilize advanced topology information and assign the importance of neighboring nodes from the perspective of graph curvature and encourages bridging the gap between graph theory and neural networks.
图神经网络 （GNN） 在许多基于图的任务中取得了巨大成功。许多工作致力于赋予 GNN 自适应定位能力，从而能够通过节点特定的机制来测量相邻节点对目标节点的重要性。但是，当前特定于节点的机制在区分节点在拓扑结构中的重要性方面存在不足。我们认为，相邻节点的结构重要性与它们在聚合中的重要性密切相关。在本文中，我们引入了离散图曲率（Ricci 曲率）来量化成对节点的结构连接的强度。我们提出了一种曲率图神经网络 （CGNN），它通过利用图曲率的结构特性有效地提高了 GNN 的自适应定位能力。为了提高曲率在各种数据集上的适应性，我们通过必要的负曲率处理模块和曲率归一化模块将曲率显式转换为相邻节点的权重。然后，我们对各种合成和真实世界的数据集进行了大量实验。在合成数据集上的实验结果表明，CGNN 有效地利用了拓扑结构信息，并且性能得到了显著提高。CGNN 在 5 个密集节点分类基准数据集上优于基线。本研究加深了对如何利用高级拓扑信息并从图曲率的角度分配相邻节点的重要性的理解，并鼓励弥合图论和神经网络之间的差距。
6.STGC-GNNs：基于 GNN 的交通预测框架，具有时空格兰杰因果关系图
It is important to model the spatial dependence of the road network for traffic prediction tasks. The essence of spatial dependence is to accurately describe how traffic information transmission is affected by other nodes in the road network, and the GNN-based traffic prediction model, as a benchmark for traffic prediction, has become the most common method for the ability to model spatial dependence by transmitting traffic information with the message passing mechanism. However, the transmission of traffic information is a global and dynamic process in long-term traffic prediction, which cannot be described by the local and static spatial dependence. In this paper, we proposed a spatial-temporal Granger causality(STGC) to model the global and dynamic spatial dependence, which can capture a stable causal relationship between nodes underlying dynamic traffic flow. The STGC can be detected by a spatial-temporal Granger causality test methods proposed by us. We chose T-GCN, STGCN and Graph Wavenet as bakbones, and the experimental results on three backbone models show that using STGC to model the spatial dependence has better results than the original model for 45-min and 1 h long-term prediction.
对于交通预测任务，对道路网络的空间依赖性进行建模非常重要。空间依赖的本质是准确描述交通信息传输如何受到路网中其他节点的影响，基于 GNN 的交通预测模型作为交通预测的基准，通过消息传递机制传输交通信息，成为对空间依赖性进行建模能力的最常用方法。然而，交通信息的传递是长期交通预测中一个全局性的、动态的过程，不能用局部和静态的空间依赖性来描述。在本文中，我们提出了一种时空格兰杰因果关系 （STGC） 来模拟全局和动态空间依赖性，它可以捕捉动态交通流底层节点之间的稳定因果关系。STGC 可以通过我们提出的时空 Granger 因果关系检验方法来检测。我们选择 T-GCN、STGCN 和 Graph Wavenet 作为 bakbones，在 3 个主干模型上的实验结果表明，使用 STGC 对空间依赖性进行建模，在 45 min 和 1 h 长期预测方面比原始模型有更好的结果。
7. 不变判别表示的无增强图对比学习
Graph contrastive learning is a promising direction toward alleviating the label dependence, poor generalization and weak robustness of graph neural networks, learning representations with invariance, and discriminability by solving pretasks. The pretasks are mainly built on mutual information estimation, which requires data augmentation to construct positive samples with similar semantics to learn invariant signals and negative samples with dissimilar semantics in order to empower representation discriminability. However, an appropriate data augmentation configuration depends heavily on lots of empirical trials such as choosing the compositions of data augmentation techniques and the corresponding hyperparameter settings. We propose an augmentation-free graph contrastive learning method, invariant-discriminative graph contrastive learning (iGCL), that does not intrinsically require negative samples. iGCL designs the invariant-discriminative loss (ID loss) to learn invariant and discriminative representations. On the one hand, ID loss learns invariant signals by directly minimizing the mean square error between the target samples and positive samples in the representation space. On the other hand, ID loss ensures that the representations are discriminative by an orthonormal constraint forcing the different dimensions of representations to be independent of each other. This prevents representations from collapsing to a point or subspace. Our theoretical analysis explains the effectiveness of ID loss from the perspectives of the redundancy reduction criterion, canonical correlation analysis, and information bottleneck principle. The experimental results demonstrate that iGCL outperforms all baselines on 5 node classification benchmark datasets. iGCL also shows superior performance for different label ratios and is capable of resisting graph attacks, which indicates that iGCL has excellent generalization and robustness.
图对比学习是一个很有前途的方向，可以减轻图神经网络的标签依赖性、泛化性差和鲁棒性弱，通过解决前任务来学习不变性表示和可判别性。前任务主要建立在互信息估计上，这需要数据增强来构建具有相似语义的正样本来学习不变信号和具有不同语义的负样本，以增强表示的可判别性。然而，适当的数据增强配置在很大程度上取决于大量的实证试验，例如选择数据增强技术的组成和相应的超参数设置。我们提出了一种无增强的图对比学习方法，即不变判别图对比学习 （iGCL），该方法本质上不需要负样本。iGCL 设计了不变判别损失 （ID loss） 来学习不变和判别表示。一方面，ID 损失通过直接最小化表示空间中目标样本和正样本之间的均方误差来学习不变信号。另一方面，ID 损失确保表示是受正交约束的判别性的，该约束迫使表示的不同维度彼此独立。这可以防止制图表达折叠到点或子空间。我们的理论分析从冗余减少标准、典型相关分析和信息瓶颈原理的角度解释了 ID 丢失的有效性。实验结果表明，iGCL 在 5 节点分类基准数据集上优于所有基线。 iGCL 在不同标签比下也表现出优异的性能，并且能够抵抗图攻击，这表明 iGCL 具有优异的泛化性和鲁棒性。
8. 用于动态图的高阶拓扑增强图卷积网络 （HoT-GCN）
Understanding the evolutionary mechanisms of dynamic graphs is crucial since dynamic is a basic characteristic of real-world networks. The challenges of modeling dynamic graphs are as follows: (1) Real-world dynamics are frequently characterized by group effects, which essentially emerge from high-order interactions involving groups of entities. Therefore, the pairwise interactions revealed by the edges of graphs are insufficient to describe complex systems. (2) The graph data obtained from real systems are often noisy, and the spurious edges can interfere with the stability and efficiency of models. To address these issues, we propose a high-order topology-enhanced graph convolutional network for modeling dynamic graphs. The rationale behind it is that the symmetric substructure in a graph, called the maximal clique, can reflect group impacts from high-order interactions on the one hand, while not being readily disturbed by spurious links on the other hand. Then, we utilize two independent branches to model the distinct influence mechanisms of the two effects. Learnable parameters are used to tune the relative importance of the two effects during the process. We conduct link predictions on real-world datasets, including one social network and two citation networks. Results show that the average improvements of the high-order enhanced methods are 68%, 15%, and 280% over the corresponding backbones across datasets. The ablation study and perturbation analysis validate the effectiveness and robustness of the proposed method. Our research reveals that high-order structures provide new perspectives for studying the dynamics of graphs and highlight the necessity of employing higher-order topologies in the future.
了解动态图的进化机制至关重要，因为动态是真实世界网络的基本特征。动态图建模的挑战如下：（1） 现实世界的动态通常以群体效应为特征，这基本上来自涉及实体组的高阶交互。因此，图边所揭示的成对交互不足以描述复杂系统。（2） 从真实系统获取的图数据往往是有噪声的，杂边会干扰模型的稳定性和效率。为了解决这些问题，我们提出了一种用于动态图建模的高阶拓扑增强图卷积网络。其背后的基本原理是，图中的对称子结构（称为最大集团）一方面可以反映高阶交互的群影响，另一方面不会轻易受到虚假链接的干扰。然后，我们利用两个独立的分支来模拟两种效应的不同影响机制。可学习参数用于在此过程中调整两种效果的相对重要性。我们在真实世界的数据集上进行链接预测，包括一个社交网络和两个引文网络。结果表明，高阶增强方法的平均改进率比数据集中的相应主干高 68%、15% 和 280%。消融研究和扰动分析验证了所提方法的有效性和稳健性。我们的研究表明，高阶结构为研究图的动力学提供了新的视角，并强调了未来采用高阶拓扑的必要性。
9. 减轻邻域偏差：用结构等效正样本增强图自我监督学习
In recent years, using a self-supervised learning framework to learn the general characteristics of graphs has been considered a promising paradigm for graph representation learning. The core of self-supervised learning strategies for graph neural networks lies in constructing suitable positive sample selection strategies. However, existing GNNs typically aggregate information from neighboring nodes to update node representations, leading to an over-reliance on neighboring positive samples, i.e., homophilous samples; while ignoring long-range positive samples, i.e., positive samples that are far apart on the graph but structurally equivalent samples, a problem we call "neighbor bias." This neighbor bias can reduce the generalization performance of GNNs. In this paper, we argue that the generalization properties of GNNs should be determined by combining homogeneous samples and structurally equivalent samples, which we call the "GC combination hypothesis." Therefore, we propose a topological signal-driven self-supervised method. It uses a topological information-guided structural equivalence sampling strategy. First, we extract multiscale topological features using persistent homology. Then we compute the structural equivalence of node pairs based on their topological features. In particular, we design a topological loss function to pull in non-neighboring node pairs with high structural equivalence in the representation space to alleviate neighbor bias. Finally, we use the joint training mechanism to adjust the effect of structural equivalence on the model to fit datasets with different characteristics. We conducted experiments on the node classification task across seven graph datasets. The results show that the model performance can be effectively improved using a strategy of topological signal enhancement.
近年来，使用自我监督学习框架来学习图的一般特征被认为是图表示学习的一种很有前途的范式。图神经网络的自监督学习策略的核心在于构建合适的正样本选择策略。然而，现有的 GNN 通常会聚合来自相邻节点的信息以更新节点表示，从而导致过度依赖相邻的阳性样本，即嗜同性样本;而忽略长程正样本，即在图上相距很远但在结构上等效样本的正样本，我们称之为“邻域偏差”的问题。这种邻域偏差会降低 GNN 的泛化性能。在本文中，我们认为 GNN 的泛化特性应该通过组合同质样本和结构等效样本来确定，我们称之为 “GC 组合假说”。因此，我们提出了一种拓扑信号驱动的自我监督方法。它使用拓扑信息引导的结构等价抽样策略。首先，我们使用持久同源性提取多尺度拓扑特征。然后，我们根据节点对的拓扑特征计算节点对的结构等效性。特别是，我们设计了一个拓扑损失函数，在表示空间中引入具有高结构等效性的非相邻节点对，以减轻邻居偏差。最后，我们使用联合训练机制来调整结构等价对模型的影响，以拟合具有不同特征的数据集。我们在 7 个图数据集上对节点分类任务进行了实验。 结果表明，采用拓扑信号增强策略可以有效提高模型性能。
10. LSTTN：用于交通流量预测的基于长短期 transformer 的时空神经网络
Accurate traffic forecasting is a fundamental problem in intelligent transportation systems and learning long-range traffic representations with key information through spatiotemporal graph neural networks (STGNNs) is a basic assumption of current traffic flow prediction models. However, due to structural limitations, existing STGNNs can only utilize short-range traffic flow data; therefore, the models cannot adequately learn the complex trends and periodic features in traffic flow. Besides, it is challenging to extract the key temporal information from the long historical traffic series and obtain a compact representation. To solve the above problems, we propose a novel LSTTN (Long-Short Term Transformer-based Network) framework comprehensively considering the long- and short-term features in historical traffic flow. First, we employ a masked subseries Transformer to infer the content of masked subseries from a small portion of unmasked subseries and their temporal context in a pretraining manner, forcing the model to efficiently learn compressed and contextual subseries temporal representations from long historical series. Then, based on the learned representations, long-term trend is extracted by using stacked 1D dilated convolution layers, and periodic features are extracted by dynamic graph convolution layers. For the difficulties in making time-step level predictions, LSTTN adopts a short-term trend extractor to learn fine-grained short-term temporal features. Finally, LSTTN fuses the long-term trend, periodic features and short-term features to obtain the prediction results. Experiments on four real-world datasets show that in 60-minute-ahead long-term forecasting, the LSTTN model achieves a minimum improvement of 5.63% and a maximum improvement of 16.78% over baseline models.
准确的交通预测是智能交通系统中的一个基本问题，通过时空图神经网络 （STGNN） 学习具有关键信息的远程交通表示是当前交通流预测模型的基本假设。然而，由于结构限制，现有的 STGNN 只能利用短距离交通流数据;因此，模型无法充分学习交通流中的复杂趋势和周期性特征。此外，从长历史流量序列中提取关键时间信息并获得紧凑的表示也具有挑战性。针对上述问题，我们提出了一种新的 LSTTN（Long-Short Term Transformer-based Network，长短期变换器网络）框架，综合考虑了历史交通流中的长期和短期特征。首先，我们采用掩码子序列 Transformer 以预训练方式从一小部分未掩码的子序列及其时间上下文中推断出掩码子序列的内容，迫使模型有效地从长历史序列中学习压缩和上下文子序列的时间表示。然后，基于学习到的表示，利用堆叠的一维膨胀卷积层提取长期趋势，通过动态图卷积层提取周期性特征。针对时间步长级预测的难点，LSTTN 采用短期趋势提取器来学习细粒度的短期时间特征。最后，LSTTN 融合长期趋势、周期特征和短期特征得到预测结果。在四个真实数据集上的实验表明，在提前 60 分钟的长期预测中，LSTTN 模型实现了 5.63% 的最小改进和最大 16% 的改进。比基线模型高 78%。
11. CAT：用于修剪异性图的因果图注意力网络
The local attention-guided message passing mechanism (LAMP) adopted in graph attention networks (GATs) can adaptively learn the importance of neighboring nodes and perform local aggregation better, thus demonstrating a stronger discrimination ability. However, existing GATs suffer from significant discrimination ability degradations in heterophilic graphs. The reason is that a high proportion of dissimilar neighbors can weaken the self-attention of the central node, resulting in the central node deviating from its similar nodes in the representation space. This type of influence caused by neighboring nodes is referred to as Distraction Effect (DE) in this paper. To estimate and weaken the DE induced by neighboring nodes, we propose a Causal graph Attention network for Trimming heterophilic graphs (CAT). To estimate the DE, since DE is generated through two paths, we adopt the total effect as the metric for estimating DE; To weaken the DE, we identify the neighbors with the highest DE (we call them Distraction Neighbors) and remove them. We adopt three representative GATs as the base model within the proposed CAT framework and conduct experiments on seven heterophilic datasets of three different sizes. Comparative experiments show that CAT can improve the node classification accuracies of all base GAT models. Ablation experiments and visualization further validate the enhanced discrimination ability of CATs. In addition, CAT is a plug-and-play framework and can be introduced to any LAMP-driven GAT because it learns a trimmed graph in the attention-learning stage, instead of modifying the model architecture or globally searching for new neighbors.
图注意力网络 （GAT） 中采用的局部注意力引导消息传递机制 （LAMP） 可以自适应地学习相邻节点的重要性，并更好地进行局部聚合，从而表现出更强的判别能力。然而，现有的 GAT 在异亲性图中遭受显着的鉴别能力下降。原因是高比例的相异邻居会削弱中心节点的自注意力，导致中心节点偏离其在表示空间中的相似节点。这种由相邻节点引起的影响在本文中称为分散效应 （DE）。为了估计和削弱由相邻节点诱导的 DE，我们提出了一个用于修剪异性图 （CAT） 的因果图注意力网络。为了估计 DE，由于 DE 是通过两条路径产生的，因此我们采用总效应作为估计 DE 的指标;为了削弱 DE，我们确定 DE 最高的邻居（我们称之为 Distraction Neighbors）并将其删除。我们在拟议的 CAT 框架内采用三个具有代表性的 GAT 作为基本模型，并在三个不同大小的 7 个异亲数据集上进行了实验。对比实验表明，CAT 可以提高所有基础 GAT 模型的节点分类精度。消融实验和可视化进一步验证了 CATs 增强的鉴别能力。此外，CAT 是一个即插即用框架，可以引入任何 LAMP 驱动的 GAT，因为它在注意力学习阶段学习修剪后的图，而不是修改模型架构或全局搜索新的邻居。
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