Dynamic Graph Convolutional Network Research Articles

DGCPPISP: a PPI site prediction model based on dynamic graph convolutional network and two-stage transfer learning

BackgroundProteins play a pivotal role in the diverse array of biological processes, making the precise prediction of protein–protein interaction (PPI) sites critical to numerous disciplines including biology, medicine and pharmacy. While deep learning methods have progressively been implemented for the prediction of PPI sites within proteins, the task of enhancing their predictive performance remains an arduous challenge.ResultsIn this paper, we propose a novel PPI site prediction model (DGCPPISP) based on a dynamic graph convolutional neural network and a two-stage transfer learning strategy. Initially, we implement the transfer learning from dual perspectives, namely feature input and model training that serve to supply efficacious prior knowledge for our model. Subsequently, we construct a network designed for the second stage of training, which is built on the foundation of dynamic graph convolution.ConclusionsTo evaluate its effectiveness, the performance of the DGCPPISP model is scrutinized using two benchmark datasets. The ensuing results demonstrate that DGCPPISP outshines competing methods in terms of performance. Specifically, DGCPPISP surpasses the second-best method, EGRET, by margins of 5.9%, 10.1%, and 13.3% for F1-measure, AUPRC, and MCC metrics respectively on Dset_186_72_PDB164. Similarly, on Dset_331, it eclipses the performance of the runner-up method, HN-PPISP, by 14.5%, 19.8%, and 29.9% respectively.

Multi-label recognition in open driving scenarios based on bipartite-driven superimposed dynamic graph

The multi-label image recognition task is widely prevalent in real-world scenarios. Overcoming the issue of overlapping and densely packed objects in complex scenes is crucial. For instance, in traffic scenarios, there are overlaps and close proximity among pedestrians, various types of vehicles, and signage. However, a primary obstacle in leveraging label relationships to enhance image classification lies in effectively integrating label semantic topology information with the image data itself. In this paper, we propose a novel framework, the Bipartite-driven Superimposed Dynamic Graph Convolutional Network (Bi-SDNet), augmented with Mapping Alignment Module (MAM) and Semantic Decoupling Module(SDM). Our approach initially decomposes input features into representations capable of discerning category label semantics at multiple scales, facilitated by MAM and SDM modules. Furthermore, through the meticulously designed Superimposed Dynamic Graph, we adeptly capture content-aware category relationships for each image, effectively modeling the relationships between these representations for the final recognition task. We conducted extensive experiments on publicly available benchmark datasets and the traffic scene dataset WZ-traffic. The model achieved an impressive 87.5% mean average precision (mAP) on the MS-COCO dataset and a commendable 91% mAP on the WZ-traffic dataset. Our research introduces novel techniques and significant breakthroughs in this field, furnishing powerful tools for enhancing model performance.

Explicit 3D reconstruction from images with dynamic graph learning and rendering-guided diffusion

High-quality 3D reconstruction is becoming increasingly important in a variety of fields. Recently, implicit representation methods have made significant progress in image-based 3D reconstruction. However, these methods tend to yield entangled neural representations which lack support for standard 3D pipelines, and their reconstruction results usually experience a sharp drop in quality when input views are reduced. To obtain high-quality 3D content, we propose an explicit 3D reconstruction method that directly extracts textured meshes from images and remains robust using reduced input views. Our central components include a dynamic graph convolutional network (GCN) and a rendering-guided diffusion model. The dynamic GCN aims to improve mesh reconstruction quality by effectively aggregating features from vertex neighborhoods. The aggregation is accelerated through sampling geometric-related neighbors with different SDF signs, which gradually converges in quantity during training. The rendering-guided diffusion model learns prior distributions for unseen regions to improve reconstruction performance using sparse-view inputs. It uses the rendered image under an interpolated camera pose as conditioned input and its diffusion strength can be controlled with the rendering loss of explicit reconstruction. In addition, the rendering-guided diffusion model can be jointly trained to generate plausible novel views with 3D consistency. Experiments demonstrate that our method can produce high-quality explicit reconstruction results and maintain realistic reconstruction using sparse-view inputs.

Spatial–Temporal Dynamic Graph Convolutional Network With Interactive Learning for Traffic Forecasting

Spatial–Temporal Dynamic Graph Convolutional Network With Interactive Learning for Traffic Forecasting

STAD-GCN: Spatial–Temporal Attention-based Dynamic Graph Convolutional Network for retail market price prediction

As technology advances, competition among market players intensifies, highlighting the importance of comprehending both one’s own and competitors’ pricing strategies. Traditional approaches often rely on static factors for price forecasting, disregarding the dynamic nature of market competition. In contrast to methods utilizing a static graph structure and uniform weights for predictions, we introduce STAD-GCN (Spatial–Temporal Attention-based Dynamic Graph Convolutional Network). This innovative model dynamically incorporates competitive factors into price prediction by employing attention mechanisms and graph convolution operations. Such an approach allows for the adaptation of graph structures and node relationships in response to temporal and spatial changes, offering a more nuanced understanding of market dynamics. To test STAD-GCN, we utilized international and domestic oil price data alongside specific gas station details from the South Korea National Oil Corporation, focusing on Seoul and Busan. The model presents remarkable performance, achieving mean absolute errors of 12.648 in Seoul and 11.242 in Busan, surpassing state-of-the-art models. Based on the findings of our research, we present some academic and practical implications, as well as several future research directions.

BIDGCN: boundary-informed dynamic graph convolutional network for adaptive spline fitting of scattered data

BIDGCN: boundary-informed dynamic graph convolutional network for adaptive spline fitting of scattered data

A performance prediction method for on-site chillers based on dynamic graph convolutional network enhanced by association rules

A performance prediction method for on-site chillers based on dynamic graph convolutional network enhanced by association rules

ADDGCN: A Novel Approach with Down-Sampling Dynamic Graph Convolution and Multi-Head Attention for Traffic Flow Forecasting

An essential component of autonomous transportation system management and decision-making is precise and real-time traffic flow forecast. Predicting future traffic conditionsis a difficult undertaking because of the intricate spatio-temporal relationships involved. Existing techniques often employ separate modules to model spatio-temporal features independently, thereby neglecting the temporally and spatially heterogeneous features among nodes. Simultaneously, many existing methods overlook the long-term relationships included in traffic data, subsequently impacting prediction accuracy. We introduce a novel method to traffic flow forecasting based on the combination of the feature-augmented down-sampling dynamic graph convolutional network and multi-head attention mechanism. Our method presents a feature augmentation mechanism to integrate traffic data features at different scales. The subsampled convolutional network enhances information interaction in spatio-temporal data, and the dynamic graph convolutional network utilizes the generated graph structure to better simulate the dynamic relationships between nodes, enhancing the model’s capacity for capturing spatial heterogeneity. Through the feature-enhanced subsampled dynamic graph convolutional network, the model can simultaneously capture spatio-temporal dependencies, and coupled with the process of multi-head temporal attention, it achieves long-term traffic flow forecasting. The findings demonstrate that the ADDGCN model demonstrates superior prediction capabilities on two real datasets (PEMS04 and PEMS08). Notably, for the PEMS04 dataset, compared to the best baseline, the performance of ADDGCN is improved by 2.46% in MAE and 2.90% in RMSE; for the PEMS08 dataset, compared to the best baseline, the ADDGCN performance is improved by 1.50% in RMSE, 3.46% in MAE, and 0.21% in MAPE, indicating our method’s superior performance.

Multi-Level Dynamic Graph Convolutional Networks for Weakly Supervised Crowd Counting

Multi-Level Dynamic Graph Convolutional Networks for Weakly Supervised Crowd Counting

Anomaly graph: leveraging dynamic graph convolutional networks for enhanced video anomaly detection in surveillance and security applications

Anomaly graph: leveraging dynamic graph convolutional networks for enhanced video anomaly detection in surveillance and security applications

DyGCN: Efficient Dynamic Graph Embedding With Graph Convolutional Network.

Graph embedding, aiming to learn low-dimensional representations (aka. embeddings) of nodes in graphs, has received significant attention. In recent years, there has been a surge of efforts, among which graph convolutional networks (GCNs) have emerged as an effective class of models. However, these methods mainly focus on the static graph embedding. In the present work, an efficient dynamic graph embedding approach is proposed, called dynamic GCN (DyGCN), which is an extension of the GCN-based methods. The embedding propagation scheme of GCN is naturally generalized to a dynamic setting in an efficient manner, which propagates the change in topological structure and neighborhood embeddings along the graph to update the node embeddings. The most affected nodes are updated first, and then their changes are propagated to further nodes, which in turn are updated. Extensive experiments on various dynamic graphs showed that the proposed model can update the node embeddings in a time-saving and performance-preserving way.

Fusion of GaoFen-5 and Sentinel-2B data for lithological mapping using vision transformer dynamic graph convolutional network

Lithological identification and mapping using remote sensing (RS) imagery are challenging. Traditional lithological mapping relies mainly on multispectral data and machine learning methods. However, inadequate spectral information and inappropriate classification algorithms are major problems for RS geological applications. Moreover, satellite hyperspectral images (HSI) at low spatial resolution and convolutional neural network (CNN)-based methods with incomplete feature extraction remain challenging because of the limitations of sensor imaging and convolutional kernels for lithological mapping. To address the above issues, in this study, smoothing filter-based intensity modulation (SFIM) fusion technology is first employed to fuse GaoFen-5 hyperspectral images and Sentinel-2B multispectral images. This approach significantly improves spatial details and enriches spectral information. Subsequently, a novel Vision Transformer Dynamic Graph Convolutional Network (ViT-DGCN) is proposed for lithological mapping of the Cuonadong dome, Tibet, China. ViT-DGCN is a joint model consisting of a transformer and a dynamic graph convolution module that enhances feature extraction capabilities by exploring long-range interaction sequence features and dynamic graph structure information in a targeted manner. The proposed algorithm exhibits superior performance compared to the others, achieving an overall accuracy of 97% for the Cuonadong dome using only 1% of the available training samples.

Traffic Flow Prediction Research Based on an Interactive Dynamic Spatial–Temporal Graph Convolutional Probabilistic Sparse Attention Mechanism (IDG-PSAtt)

Accurate traffic flow prediction is highly important for relieving road congestion. Due to the intricate spatial–temporal dependence of traffic flows, especially the hidden dynamic correlations among road nodes, and the dynamic spatial–temporal characteristics of traffic flows, a traffic flow prediction model based on an interactive dynamic spatial–temporal graph convolutional probabilistic sparse attention mechanism (IDG-PSAtt) is proposed. Specifically, the IDG-PSAtt model consists of an interactive dynamic graph convolutional network (IL-DGCN) with a spatial–temporal convolution (ST-Conv) block and a probabilistic sparse self-attention (ProbSSAtt) mechanism. The IL-DGCN divides the time series of a traffic flow into intervals and synchronously and interactively shares the captured dynamic spatiotemporal features. The ST-Conv block is utilized to capture the complex dynamic spatial–temporal characteristics of the traffic flow, and the ProbSSAtt block is utilized for medium-to-long-term forecasting. In addition, a dynamic GCN is generated by fusing adaptive and learnable adjacency matrices to learn the hidden dynamic associations among road network nodes. Experimental results demonstrate that the IDG-PSAtt model outperforms the baseline methods in terms of prediction accuracy. Specifically, on METR-LA, the mean absolute error (MAE) and root mean square error (RMSE) induced by IDG-PSAtt for a 60 min forecasting scenario are reduced by 0.75 and 1.31, respectively, compared to those of the state-of-the-art models. This traffic flow prediction improvement will lead to more precise estimates of the emissions produced by mobile sources, resulting in more accurate air quality forecasts. Consequently, this research will greatly support local environmental management efforts.

DGFormer: Dynamic graph transformer for 3D human pose estimation

Despite the significant progress for monocular 3D human pose estimation, it still faces challenges due to self-occlusions and depth ambiguities. To tackle those issues, we propose a novel Dynamic Graph Transformer (DGFormer) to exploit local and global relationships between skeleton joints for pose estimation. Specifically, the proposed DGFormer mainly consists of three core modules: Transformer Encoder (TE), immobile Graph Convolutional Network (GCN), and dynamic GCN. TE module leverages the self-attention mechanism to learn the complex global relationships among skeleton joints. The immobile GCN is responsible for capturing the local physical connections between human joints, while the dynamic GCN concentrates on learning the sparse dynamic K-nearest neighbor interactions according to different action poses. By building the adequately global long-range, local physical, and sparse dynamic dependencies of human joints, experiments on Human3.6M and MPI-INF-3DHP datasets demonstrate that our method can predict 3D pose with lower errors outperforming the recent state-of-the-art image-based performance. Furthermore, experiments on in-the-wild videos demonstrate the impressive generalization abilities of our method. Code will be available at: https://github.com/czmmmm/DGFormer.

A Novel Money Laundering Prediction Model Based on a Dynamic Graph Convolutional Neural Network and Long Short-Term Memory

Money laundering is an illicit activity that seeks to conceal the nature and origins of criminal proceeds, posing a substantial threat to the national economy, the political order, and social stability. To scientifically and reasonably predict money laundering risks, this paper focuses on the “layering” stage of the money laundering process in the field of supervised learning for money laundering fraud prediction. A money laundering and fraud prediction model based on deep learning, referred to as MDGC-LSTM, is proposed. The model combines the use of a dynamic graph convolutional network (MDGC) and a long short-term memory (LSTM) network to efficiently identify illegal money laundering activities within financial transactions. MDGC-LSTM constructs dynamic graph snapshots with symmetrical spatiotemporal structures based on transaction information, representing transaction nodes and currency flows as graph nodes and edges, respectively, and effectively captures the relationships between temporal and spatial structures, thus achieving the dynamic prediction of fraudulent transactions. The experimental results demonstrate that compared with traditional algorithms and other deep learning models, MDGC-LSTM achieves significant advantages in comprehensive spatiotemporal feature modeling. Specifically, based on the Elliptic dataset, MDGC-LSTM improves the Macro-F1 score by 0.25 compared to that of the anti-money laundering fraud prediction model currently considered optimal.

A graph convolutional network with dynamic weight fusion of multi-scale local features for diabetic retinopathy grading

Diabetic retinopathy (DR) is a serious ocular complication that can pose a serious risk to a patient’s vision and overall health. Currently, the automatic grading of DR is mainly using deep learning techniques. However, the lesion information in DR images is complex, variable in shape and size, and randomly distributed in the images, which leads to some shortcomings of the current research methods, i.e., it is difficult to effectively extract the information of these various features, and it is difficult to establish the connection between the lesion information in different regions. To address these shortcomings, we design a multi-scale dynamic fusion (MSDF) module and combine it with graph convolution operations to propose a multi-scale dynamic graph convolutional network (MDGNet) in this paper. MDGNet firstly uses convolution kernels with different sizes to extract features with different shapes and sizes in the lesion regions, and then automatically learns the corresponding weights for feature fusion according to the contribution of different features to model grading. Finally, the graph convolution operation is used to link the lesion features in different regions. As a result, our proposed method can effectively combine local and global features, which is beneficial for the correct DR grading. We evaluate the effectiveness of method on two publicly available datasets, namely APTOS and DDR. Extensive experiments demonstrate that our proposed MDGNet achieves the best grading results on APTOS and DDR, and is more accurate and diverse for the extraction of lesion information.

Traffic Flow Prediction Based on Interactive Dynamic Spatio-Temporal Graph Convolution with a Probabilistic Sparse Attention Mechanism

Accurate traffic flow prediction is of great practical significance to alleviate road congestion. Existing methods ignore the hidden dynamic associations between road nodes, and for the problem of difficulty capturing the dynamic spatio-temporal features of traffic flow in the prediction process, a novel model based on the interactive dynamic spatio-temporal graph convolutional probabilistic sparse attention mechanism (IDG-PSAtt) is proposed, which consists of an interactive dynamic graph convolutional network (IDGCN) structure with a spatio-temporal convolutional block (ST-Conv block) and a probabilistic sparse self-attention mechanism block (ProbSSAtt block). Among them, the IDGCN synchronizes the dynamic spatio-temporal features captured by interaction sharing, and the ST-Conv block is combined with the ProbSSAtt block to effectively capture the long short-term temporal features of the traffic flow. In addition, to effectively find the hidden dynamic associations between road network nodes, a dynamic graph convolutional network generated by the fusion of an adaptive neighbor matrix and a learnable neighbor matrix was constructed. Experimental results demonstrate that the prediction performance of the IDG-PSAtt model outperforms the baseline model under the evaluation criteria and experimental settings given in this paper. In the PEMS-BAY dataset, the mean absolute error and root mean square error of the IDG-PSAtt to 60 min are improved by 15.49% and 12.10%, compared with the state-of-the-art model, respectively.

DTM-GCN: A traffic flow prediction model based on dynamic graph convolutional network

DTM-GCN: A traffic flow prediction model based on dynamic graph convolutional network

Multimodal dynamic graph convolutional network for crowdfunding success prediction

Crowdfunding creates opportunities for creative people to raise funds so their ideas can be brought to life. However, the failure of fundraising leads to certain losses for project starters. Crowdfunding success prediction allows them to learn about the probability of fundraising success as early as possible, which can reduce their losses or lead them to modify project content for increasing the probability. Crowdfunding success prediction is a challenging classification task because there is diverse descriptive data but relative little supervisory information. There has been a lot of work done based on post-launch factors, but pre-launch prediction is more valuable for project creators and crowdfunding platforms. Although some efforts on pre-launch prediction were also made, most of them only analyze one or more of metadata, text and image, and the multimodal study about text and video is lacked. Considering this, we propose a multimodal dynamic graph convolutional network for crowdfunding success prediction based on text and video data. Specifically, sentences and frames are viewed as nodes, and edges are constructed based on location relationships and cosine similarity, which reconstructs the text and video into a multimodal graph and allows multimodal features to interact in the form of a graph. Besides, dynamic graph convolution is utilized to fuse the features of nodes, where dynamic means the structure of graph changes during graph convolution. Besides, we make two multimodal crowdfunding datasets containing metadata, text, image, video and project status, and they are utilized to validate the effectiveness of crowdfunding prediction models. Experiments conducted on them show that our model outperforms existing state-of-the-art baselines, and interaction and fusion in the form of a graph can effectively integrate features from multiple modalities.

Multi-scale feature learning via residual dynamic graph convolutional network for hyperspectral image classification

ABSTRACT Thanks to the relevant spectral-spatial information, hyperspectral images (HSIs) have been widely exploited in Earth observation. Recently, graph convolutional networks (GCNs) have attracted increasing attention in HSI classification due to their advantages in processing non-Euclidean structure data. Unlike convolutional neural networks (CNNs), which perform convolution operations on regular square regions, GCNs can directly work on graph structure data to extract the relationships among adjacent land covers. However, extracting meaningful and deep discriminative spectral-spatial features from HSIs is still a challenging task. In this article, a novel multi-scale feature learning via residual dynamic graph convolutional network is designed for HSI classification, which can extract large-scale contextual spatial structures at superpixel-level graph and local spectral-spatial information at pixel-level, significantly improving the performance of HSI classification. Unlike from the existing GCN-based methods that operate on a graph with a fixed neighbourhood size, multiple graphs with diverse neighbourhood scales are built to comprehensively leverage spectral-spatial information and relationship at multiple scales, and these graphs are dynamically updated to generate more discriminative features (via dynamic GCN) during the convolution process. Moreover, to fully use the multi-scale features extracted from HSIs, a multi-scale feature fusion module is developed to emphasize important features and suppress irrelevant ones. Extensive experiments carried on three benchmark data sets demonstrate the superiority of the proposed approach over other state-of-the-art methods.