Dynamic Graph Convolutional Network Research Articles

Dynamic graph convolution neural network based on spatial-temporal correlation for air quality prediction

Air pollution is a serious threat to both the ecological environment and the physical health of individuals. Therefore, accurate air quality prediction is urgent and necessary for pollution mitigation and residents’ travel. However, few existing models are established based on the dynamic spatiotemporal correlation of air pollutants to predict air quality. In this paper, a novel deep learning model combining the dynamic graph convolutional network and the multi-channel temporal convolutional network (DGC-MTCN) is proposed for air quality prediction. To efficiently represent the time-varying spatial dependencies, a new spatiotemporal dynamic correlation calculation method based on gray relation analysis is proposed to construct dynamic adjacency matrices. Then, the spatiotemporal features are sufficiently extracted by the graph convolutional network and the multi-channel temporal convolutional network. Two real-world air quality datasets collected from Beijing and Fushun are applied to verify the performance of our proposed model. The experimental results show that compared with other baselines, the DGC-MTCN model has excellent prediction accuracy. Especially for the prediction of multi-step and different stations, our model performs better temporal stability and generalization ability.

DDGCN: Dual Dynamic Graph Convolutional Networks for Rumor Detection on Social Media

Detecting rumors on social media has become particular important due to the rapid dissemination and adverse impacts on our lives. Though a set of rumor detection models have exploited the message propagation structural or temporal information, they seldom model them altogether to enjoy the best of both worlds. Moreover, the dynamics of knowledge information associated with the comments are not involved, either. To this end, we propose a novel Dual-Dynamic Graph Convolutional Networks, termed as DDGCN, which can model the dynamics of messages in propagation as well as the dynamics of the background knowledge from Knowledge graphs in one unified framework. Specifically, two Graph Convolutional Networks are adopted to capture the above two types of structure information at different time stages, which are then combined with a temporal fusing unit. This allows for learning the dynamic event representations in a more fine-grained manner, and incrementally aggregating them to capture the cascading effect for better rumor detection. Extensive experiments on two public real-world datasets demonstrate that our proposal yields significant improvements compared to strong baselines and can detect rumors at early stages.

Dynamic graph convolutional networks based on spatiotemporal data embedding for traffic flow forecasting

Traffic flow forecasting has always been a challenge owing to its complicated spatiotemporal dependencies. Few of previous works can exploit the implicit interactions among traffic flows, leading to the superficial extraction of spatiotemporal features. Though graph convolutional networks have showed exciting performance in traffic flow forecasting, existing works either ignore the dynamic characteristics of the correlations among sensors or fail to extract the hidden fine-grained correlations among sensors, which makes it difficult to model the spatial dependency deeply. Therefore, a new deep learning model is proposed in this study to overcome these drawbacks and to achieve accurate traffic flow forecasting. First, a new spatiotemporal data embedding method is proposed to convert the original traffic flows into traffic flow vectors, so that the implicit correlations among traffic flows can be quantified and measured. Then, to sufficiently extract the non-linear global temporal features, a new temporal vector convolutional neural network is proposed to deal with the traffic flow vectors. Finally, a new dynamic correlation graph construction method is proposed to exploit the dynamic characteristics of correlations among sensors and explore the hidden fine-grained correlations among sensors, which is conducive to learning deep non-Euclidean spatial features. Experiments on five traffic datasets demonstrate that the proposed model is superior to state-of-the-art baseline models.

DEEP FEATURE EXTRACTION BASED ON DYNAMIC GRAPH CONVOLUTIONAL NETWORKS FOR ACCELERATED HYPERSPECTRAL IMAGE CLASSIFICATION

Abstract. Deep learning has achieved impressive results on hyperspectral images (HSIs) classification. Among them, supervised learning convolutional neural networks (CNNs) and semi-supervised learning graph neural networks (GNNs) are the two main network frameworks. However, 1) the supervised learning CNN faces the problem of high model time complexity as the number of network layers deepens; 2) the semi-supervised learning GNN faces the problem of high spatial complexity due to the computation of adjacency relations. In this paper, a novel dynamic graph convolutional HSI classification method is proposed, which is called dynamic graph convolutional networks (DGCNet). We first obtain two classification features by implementing flattening and global average pooling operation on the results of the convolution layer, which fully exploits the spatial-spectral information contained in the hyperspectral data. Then the dynamic graph convolution module is applied to extract the intrinsic structural information of each patch. Finally, HSI is classified based on spatial, spectral and structural features. DGCNet uses three branches to process multiple features of HSI in parallel and is trained in a supervised learning manner. In addition, DropBlock and label smoothing regularization techniques are applied to further improve the generalization capability of the model. Comparative experiments show that our proposed algorithm is comparable with the state-of-the art supervised learning models in terms of accuracy while also significantly outperforming in terms of time.

DEDGCN: Dual Evolving Dynamic Graph Convolutional Network

With the wide application of graph data in many fields, the research of graph representation learning technology has become the focus of scholars’ attention. Especially, dynamic graph representation learning is an important part of solving the problem of change graph in reality. On the one hand, most dynamic graph representation methods focus either on graph structure changes or node embedding changes, ignoring the internal relationship. On the other hand, most dynamic graph neural networks require learn node embeddings from specific tasks, resulting in poor universality of node embeddings and cannot be used in unsupervised tasks. Hence, Dual Evolving Dynamic Graph Convolutional Network (DEDGCN) was proposed to solve the above problems. DEDGCN uses the recurrent neural network to push the evolvement of GCN and nodes, from which it can extract the structural features of dynamic graph and learns the stability features of nodes, respectively, forming an adaptive dynamic graph convolution network. DEDGCN can be classified as unsupervised graph convolutional network. Thus, it is capable of training the unlabeled dynamic graph, it has more extensive application scenarios, and the calculated node embedding has strong generality. We evaluate our proposed method on experimental data in three tasks which are node classification, edge classification, and link prediction. In the classification task, facing the graph with large scale, complex connection relationship, and uncertain change rule, the F1 value of node classification task obtained by DEDGCN reaches 77%, and the F1 value of edge classification task reaches more than 90%. The results show that DEDGCN is effective in capturing graph features, and the effect of DEDGCN is much higher than other baseline methods, which proves the importance of capturing node stability features in dynamic graph representation learning. At the same time, the ability of DEDGCN in unsupervised tasks is further verified by using clustering and anomaly detection tasks, which proves that DEDGCN learning network embedding is widely used.

Dynamic graph convolutional network for assembly behavior recognition based on attention mechanism and multi-scale feature fusion.

Intelligent recognition of assembly behaviors of workshop production personnel is crucial to improve production assembly efficiency and ensure production safety. This paper proposes a graph convolutional network model for assembly behavior recognition based on attention mechanism and multi-scale feature fusion. The proposed model learns the potential relationship between assembly actions and assembly tools for recognizing assembly behaviors. Meanwhile, the introduction of an attention mechanism helps the network to focus on the key information in assembly behavior images. Besides, the multi-scale feature fusion module is introduced to enable the network to better extract image features at different scales. This paper constructs a data set containing 15 types of workshop production behaviors, and the proposed assembly behavior recognition model is tested on this data set. The experimental results show that the proposed model achieves good recognition results, with an average assembly recognition accuracy of 93.1%.

An Invertible Dynamic Graph Convolutional Network for Multi-Center ASD Classification.

Autism Spectrum Disorder (ASD) is one common developmental disorder with great variations in symptoms and severity, making the diagnosis of ASD a challenging task. Existing deep learning models using brain connectivity features to classify ASD still suffer from degraded performance for multi-center data due to limited feature representation ability and insufficient interpretability. Given that Graph Convolutional Network (GCN) has demonstrated superiority in learning discriminative representations of brain connectivity networks, in this paper, we propose an invertible dynamic GCN model to identify ASD and investigate the alterations of connectivity patterns associated with the disease. In order to select explainable features from the model, invertible blocks are introduced in the whole network, and we are able to reconstruct the input dynamic features from the network's output. A pre-screening of connectivity features is adopted to reduce the redundancy of the input information, and a fully-connected layer is added to perform classification. The experimental results on 867 subjects show that our proposed method achieves superior disease classification performance. It provides an interpretable deep learning model for brain connectivity analysis and is of great potential in studying brain-related disorders.

DRGCN: Dual Residual Graph Convolutional Network for Hyperspectral Image Classification

Recently, graph convolutional network (GCN) has drawn increasing attention in hyperspectral image (HSI) classification, as it can process arbitrary non-Euclidean data. However, dynamic GCN that refines the graph heavily relies on the graph embedding in the previous layer, which will result in performance degradation when the embedding contains noise. In this letter, we propose a novel dual residual graph convolutional network (DRGCN) for HSI classification that integrates two adjacency matrices of dual GCN. In detail, one GCN applies a soft adjacency matrix to extract spatial features, the other utilizes the dynamic adjacency matrix to extract global context-aware features. Subsequently, the features extracted by dual GCN are fused to make full use of the complementary and correlated information among two graph representations. Moreover, we introduce residual learning to optimize graph convolutional layers during the training process, to alleviate the over-smoothing problem. The advantage of dual GCN is that it can extract robust and discriminative features from HSI. Extensive experiments on four HSI data sets, including Indian Pines, Pavia University, Salinas, and Houston University, demonstrate the effectiveness and superiority of our proposed DRGCN, even with small-sized training data.

Dynamic Graph Convolutional Network for Long Short-Term Traffic Flow Prediction

Dynamic Graph Convolutional Network for Long Short-Term Traffic Flow Prediction

An Unsupervised Remote Sensing Change Detection Method Based on Multiscale Graph Convolutional Network and Metric Learning

As a fundamental application, change detection (CD) is widespread in the remote sensing (RS) community. With the increase in the spatial resolution of RS images, high-resolution remote sensing (HRRS) image CD tasks receive growing attention. The change information hidden in multitemporal HRRS images could help discover our planet comprehensively. In the current deep learning era, convolutional neural networks (CNNs) have become one of the most powerful tools for a wide range of RS tasks including HRRS image CD, due to their superb feature learning capacity. However, most of them need a large amount of labeled data to accomplish the CD process, which is challenging or even impractical in many RS applications. Also, given the limited valid receptive field, CNNs can only capture short-range context within HRRS images, which is probably not enough to fully explore change information from the images. To overcome these limitations, in this article, we propose an unsupervised CD method, termed GMCD, based on graph convolutional network (GCN) and metric learning. GMCD consists of a Siamese fully convolution network (FCN), a multiscale dynamic GCN (Mlt-GCN), and a pseudolabel generation mechanism based on metric learning. The Siamese FCN contains a Siamese encoder and a pyramid-shaped decoder, aiming to extract multiscale features and integrate them to generate reliable difference images (DIs). Mlt-GCN focuses on capturing the short- and long-range contextual patterns at feature map level to extract changed and unchanged areas completely. The pseudolabel generation mechanism aims to produce reliable pseudolabels (changed, unchanged, and uncertain) to help accomplish the model training in an unsupervised way. Experiments on four HRRS image CD datasets demonstrate that GMCD outperforms the existing state-of-the-art methods.

A dynamic graph convolutional neural network framework reveals new insights into connectome dysfunctions in ADHD

The pathological mechanism of attention deficit hyperactivity disorder (ADHD) is incompletely specified, which leads to difficulty in precise diagnosis. Functional magnetic resonance imaging (fMRI) has emerged as a common neuroimaging technique for studying the brain functional connectome. Most existing methods that have either ignored or simply utilized graph structure, do not fully leverage the potentially important topological information which may be useful in characterizing brain disorders. There is a crucial need for designing novel and efficient approaches which can capture such information. To this end, we propose a new dynamic graph convolutional network (dGCN), which is trained with sparse brain regional connections from dynamically calculated graph features. We also develop a novel convolutional readout layer to improve graph representation. Our extensive experimental analysis demonstrates significantly improved performance of dGCN for ADHD diagnosis compared with existing machine learning and deep learning methods. Visualizations of the salient regions of interest (ROIs) and connectivity based on informative features learned by our model show that the identified functional abnormalities mainly involve brain regions in temporal pole, gyrus rectus, and cerebellar gyri from temporal lobe, frontal lobe, and cerebellum, respectively. A positive correlation was further observed between the identified connectomic abnormalities and ADHD symptom severity. The proposed dGCN model shows great promise in providing a functional network-based precision diagnosis of ADHD and is also broadly applicable to brain connectome-based study of mental disorders.

Dynamic graph convolutional networks with attention mechanism for rumor detection on social media

Social media has become an ideal platform for the propagation of rumors, fake news, and misinformation. Rumors on social media not only mislead online users but also affect the real world immensely. Thus, detecting the rumors and preventing their spread became an essential task. Some of the recent deep learning-based rumor detection methods, such as Bi-Directional Graph Convolutional Networks (Bi-GCN), represent rumor using the completed stage of the rumor diffusion and try to learn the structural information from it. However, these methods are limited to represent rumor propagation as a static graph, which isn’t optimal for capturing the dynamic information of the rumors. In this study, we propose novel graph convolutional networks with attention mechanisms, named Dynamic GCN, for rumor detection. We first represent rumor posts with their responsive posts as dynamic graphs. The temporal information is used to generate a sequence of graph snapshots. The representation learning on graph snapshots with attention mechanism captures both structural and temporal information of rumor spreads. The conducted experiments on three real-world datasets demonstrate the superiority of Dynamic GCN over the state-of-the-art methods in the rumor detection task.

Interpretable learning based Dynamic Graph Convolutional Networks for Alzheimer’s Disease analysis

Graph Convolutional Networks (GCNs) are widely applied in classification tasks by aggregating the neighborhood information of each sample to output robust node embedding. However, conventional GCN methods do not update the graph during the training process so that their effectiveness is always influenced by the quality of the input graph. Moreover, previous GCN methods lack the interpretability to limit their real applications. In this paper, a novel personalized diagnosis technique is proposed for early Alzheimer’s Disease (AD) diagnosis via coupling interpretable feature learning with dynamic graph learning into the GCN architecture. Specifically, the module of interpretable feature learning selects informative features to provide interpretability for disease diagnosis and abandons redundant features to capture inherent correlation of data points. The module of dynamic graph learning adjusts the neighborhood relationship of every data point to output robust node embedding as well as the correlations of all data points to refine the classifier. The GCN module outputs diagnosis results based on the learned inherent graph structure. All three modules are jointly optimized to perform reliable disease diagnosis at an individual level. Experiments demonstrate that our method outputs competitive diagnosis performance as well as provide interpretability for personalized disease diagnosis.

Dynamic graph convolutional network for long-term traffic flow prediction with reinforcement learning

Exploiting deep learning techniques for traffic flow prediction has become increasingly widespread. Most existing studies combine CNN or GCN with recurrent neural network to extract the spatio-temporal features in traffic networks. The traffic networks can be naturally modeled as graphs which are effective to capture the topology and spatial correlations among road links. The issue is that the traffic network is dynamic due to the continuous changing of the traffic environment. Compared with the static graph, the dynamic graph can better reflect the spatio-temporal features of the traffic network. However, in practical applications, due to the limited accuracy and timeliness of data, it is hard to generate graph structures through frequent statistical data. Therefore, it is necessary to design a method to overcome data defects in traffic flow prediction. In this paper, we propose a long-term traffic flow prediction method based on dynamic graphs. The traffic network is modeled by dynamic traffic flow probability graphs, and graph convolution is performed on the dynamic graphs to learn spatial features, which are then combined with LSTM units to learn temporal features. In particular, we further propose to use graph convolutional policy network based on reinforcement learning to generate dynamic graphs when the dynamic graphs are incomplete due to the data sparsity i sue. By testing our method on city-bike data in New York City, it demonstrates that our model can achieve stable and effective long-term predictions of traffic flow, and can reduce the impact of data defects on prediction results.

Robust and Dynamic Graph Convolutional Network For Multi-view Data Classification

AbstractSince graph learning could preserve the structure information of the samples to improve the learning ability, it has been widely applied in both shallow learning and deep learning. However, the current graph learning methods still suffer from the issues such as outlier influence and model robustness. In this paper, we propose a new dynamic graph neural network (DGCN) method to conduct semi-supervised classification on multi-view data by jointly conducting the graph learning and the classification task in a unified framework. Specifically, our method investigates three strategies to improve the quality of the graph before feeding it into the GCN model: (i) employing robust statistics to consider the sample importance for reducing the outlier influence, i.e. assigning every sample with soft weights so that the important samples are with large weights and outliers are with small or even zero weights; (ii) learning the common representation across all views to improve the quality of the graph for every view; and (iii) learning the complementary information from all initial graphs on multi-view data to further improve the learning of the graph for every view. As a result, each of the strategies could improve the robustness of the DGCN model. Moreover, they are complementary for reducing outlier influence from different aspects, i.e. the sample importance reduces the weights of the outliers, both the common representation and the complementary information improve the quality of the graph for every view. Experimental result on real data sets demonstrates the effectiveness of our method, compared to the comparison methods, in terms of multi-class classification performance.

Multistep traffic forecasting by dynamic graph convolution: Interpretations of real-time spatial correlations

Accurate and explainable short-term traffic forecasting is pivotal for making trustworthy decisions in advanced traffic control and guidance systems. Recently, deep learning approach, as a data-driven alternative to traffic flow model-based data assimilation and prediction methods, has become popular in this domain. Many of these deep learning models show promising predictive performance, but inherently suffer from a lack of interpretability. This difficulty largely originates from the inconsistency between the static input–output mappings encoded in deep neural networks and the dynamic nature of traffic phenomena. Under different traffic conditions, such as freely-flowing versus heavily congested traffic, different mappings are needed to predict the propagation of congestion and the resulting speeds over the network more accurately. In this study, we design a novel variant of the graph attention mechanism. The major innovation of this so-called dynamic graph convolution (DGC) module is that local area-wide graph convolutional kernels are dynamically generated from evolving traffic states to capture real-time spatial dependencies. When traffic conditions change, the spatial correlation encoded by DGC module changes as well. Using the DGC, we propose a multistep traffic forecasting model, the Dynamic Graph Convolutional Network (DGCN). Experiments using real freeway data show that the DGCN has a competitive predictive performance compared to other state-of-the-art models. Equally importantly, the prediction process in the DGCN and the trained parameters are indeed explainable. It turns out that the DGCN learns to mimic the upstream–downstream asymmetric information flow of typical road traffic operations. Specifically, there exists a speed-dependent optimal receptive field – which governs what information the DGC kernels assimilate – that is consistent with the back-propagation speed of stop-and-go waves in traffic streams. This implies that the learnt parameters are consistent with traffic flow theory. We believe that this research paves a path to more transparent deep learning models applied for short-term traffic forecasting.

Multi-filter dynamic graph convolutional networks for skeleton-based action recognition

Abstract Action recognition plays an important role in video understanding, and the dynamic changes of human bones provide important information for it. In order to solve the problem that the source point can only obtain the characteristics of adjacent nodes and the static structure of the skeleton diagram can not meet the adaptive requirements, this paper proposes an Inception structure and dynamic skeleton diagram based on the convolutional neural network, namely, the multi-filter dynamic graph convolutional neural network. The method can not only enable the source point to obtain the characteristics of distant nodes, but also can use the dynamic skeleton diagram structure to generate different body associations for different actions, which improves the adaptability and accuracy of the model and achieves better results.

Point Cloud Classification and Segmentation Network Based on Dynamic Graph Convolutional Network

Point Cloud Classification and Segmentation Network Based on Dynamic Graph Convolutional Network

Human activity recognition by manifold regularization based dynamic graph convolutional networks

Deep learning has shown superiority to extract more representative features from multimedia data in recent years. Recently, the most typical graph convolutional networks (GCN) has achieved excellent performance in the semi-supervised framework-based data representation learning tasks. GCN successfully generalizes traditional convolutional neural networks to encode arbitrary graphs by exploiting the graph Laplacian-based sample structure information. However, GCN only fuses the static structure information. It is difficult to guarantee that its structure information is optimal during the training process and applicable for all practical applications. To tackle the above problem, in this paper, we propose a manifold regularized dynamic graph convolutional network (MRDGCN). The proposed MRDGCN automatically updates the structure information by manifold regularization until model fitting. In particular, we build an optimization convolution layer formulation to acquire the optimal structure information. Thus, MRDGCN can automatically learn high-level sample features to improve the performance of data representation learning. To demonstrate the effectiveness of our proposed model, we apply MRDGCN on the semi-supervised classification tasks. The extensive experiment results on human activity datasets and citation network datasets validate the performance of MRDGCN compared with GCN and other semi-supervised learning methods.

Dynamic graph convolutional network for multi-video summarization

Multi-video summarization is an effective tool for users to browse multiple videos. In this paper, multi-video summarization is formulated as a graph analysis problem and a dynamic graph convolutional network is proposed to measure the importance and relevance of each video shot in its own video as well as in the whole video collection. Two strategies are proposed to solve the inherent class imbalance problem of video summarization task. Moreover, we propose a diversity regularization to encourage the model to generate a diverse summary. Extensive experiments are conducted, and the comparisons are carried out with the state-of-the-art video summarization methods, the traditional and novel graph models. Our method achieves state-of-the-art performances on two standard video summarization datasets. The results demonstrate the effectiveness of our proposed model in generating a representative summary for multiple videos with good diversity.