In this paper, we propose a novel model that is based on a hybrid paradigm composed of a graph convolution network and an Integer Programming solver. The model utilizes the potential of graph neural networks, which have the ability to capture complex...
For achieving seamless global coverage and real-time communications while providing intelligent applications with increased quality of service (QoS), AI-enabled space–air–ground integrated networks (SAGINs) have attracted widespread atten...
Accurate urban traffic flow prediction plays a vital role in Intelligent Transportation System (ITS). The complex long-term and long-range spatiotemporal correlations of traffic flow pose a significant challenge to the prediction task. Most current r...
Weak magnetic flux leak detection is one of the most important non-destructive testing and measurement methods for pipelines. Since different defects cause different damage, it is necessary to classify the different types of defects. Traditional mach...
In recent years, cellular communication systems have continued to develop in the direction of intelligence. The demand for cellular networks is increasing as they meet the public’s pursuit of a better life. Accurate prediction of cellular netwo...
Exploring the delay causality between airports and comparing the delay propagation patterns across different airport networks is critical to better understand delay propagation mechanisms and provide effective delay mitigation strategies. A novel att...
In recent years, Academia and industry have conducted extensive and in-depth research on bearing-fault-diagnosis technology. However, the current modeling of time–space coupling characteristics in rolling bearing fault diagnosis remains inadequ...
Graph convolutional networks (GCNs) have attracted increasing attention in various fields due to their significant capacity to process graph-structured data. Typically, the GCN model and its variants heavily rely on the transmission of node features...
Accurate traffic prediction is crucial to the construction of intelligent transportation systems. This task remains challenging because of the complicated and dynamic spatiotemporal dependency in traffic networks. While various graph-based spatiotemp...
Semi-supervised graph convolutional networks (SSGCNs) have been proven to be effective in hyperspectral image classification (HSIC). However, limited training data and spectral uncertainty restrict the classification performance, and the computationa...
The Graph Convolutional Neural Networks (GCN) method has shown excellent performance in the field of deep learning, and using graphs to represent speech data is a computationally efficient and scalable approach. In order to enhance the adequacy of gr...
Accurate and real-time traffic speed prediction remains challenging due to the irregularity and asymmetry of real-traffic road networks. Existing models based on graph convolutional networks commonly use multi-layer graph convolution to extract an un...
With the quick development of network technology, the number of active IoT devices is growing rapidly. Numerous network scanning organizations have emerged to scan and detect network assets around the clock. This greatly facilitates illegal cyberatta...
Studying gene regulatory networks (GRNs) is paramount for unraveling the complexities of biological processes and their associated disorders, such as diabetes, cancer, and Alzheimer’s disease. Recent advancements in computational biology have a...
Walking is an exercise that uses muscles and joints of the human body and is essential for understanding body condition. Analyzing body movements through gait has been studied and applied in human identification, sports science, and medicine. This st...
In speech emotion recognition (SER), our research addresses the critical challenges of capturing and evaluating node information and their complex interrelationships within speech data. We introduce Skip Graph Convolutional and Graph Attention Networ...
Graph convolutional networks (GCN) have emerged as a powerful alternative tool for analyzing hyperspectral images (HSIs). Despite their impressive performance, current works strive to make GCN more sophisticated through either elaborate architecture...
Machine learning and deep learning methods have been employed in the hyperspectral image (HSI) classification field. Of deep learning methods, convolution neural network (CNN) has been widely used and achieved promising results. However, CNN has its...
Suffering from the multi-view data diversity and complexity, most of the existing graph convolutional networks focus on the networks’ architecture construction or the salient graph structure preservation for node classification in citation netw...
Aberrant expressions of long non-coding RNAs (lncRNAs) are often associated with diseases and identification of disease-related lncRNAs is helpful for elucidating complex pathogenesis. Recent methods for predicting associations between lncRNAs and di...
Given the escalating frequency and severity of global forest fires, it is imperative to develop advanced detection and segmentation technologies to mitigate their impact. To address the challenges of these technologies, the development of deep learni...
Convolutional neural networks (CNNs) have attracted significant attention as a commonly used method for hyperspectral image (HSI) classification in recent years; however, CNNs can only be applied to Euclidean data and have difficulties in dealing wit...
Graph Neural Networks (GNNs) are neural networks that learn the representation of nodes and associated edges that connect it to every other node while maintaining graph representation. Graph Convolutional Neural Networks (GCNs), as a representative m...
Deep learning technology, such as fully convolutional networks (FCNs), have shown competitive performance in the automatic extraction of buildings from high-resolution aerial images (HRAIs). However, there are problems of over-segmentation and intern...
Knowledge graph embedding can learn low-rank vector representations for knowledge graph entities and relations, and has been a main research topic for knowledge graph completion. Several recent works suggest that convolutional neural network (CNN)-ba...
As the use of digital currencies, such as cryptocurrencies, increases in popularity, phishing scams and other cybercriminal activities on blockchain platforms (e.g., Ethereum) have also risen. Current methods of detecting phishing in Ethereum focus m...
Graph convolutional networks (GCNs) are neural network frameworks for machine learning on graphs. They can simultaneously perform end-to-end learning on the attribute information and the structure information of graph data. However, most existing GCN...
Applying machine learning algorithms to graph-structured data has garnered significant attention in recent years due to the prevalence of inherent graph structures in real-life datasets. However, the direct application of traditional deep learning al...
In hyperspectral image (HSI) classification, convolutional neural networks (CNN) have been attracting increasing attention because of their ability to represent spectral-spatial features. Nevertheless, the conventional CNN models perform convolution...
Traffic forecasting has always been an important part of intelligent transportation systems. At present, spatiotemporal graph neural networks are widely used to capture spatiotemporal dependencies. However, most spatiotemporal graph neural networks u...
The electroencephalogram (EEG), widely used for measuring the brain’s electrophysiological activity, has been extensively applied in the automatic detection of epileptic seizures. However, several challenges remain unaddressed in prior studies...
The immense representation power of deep learning frameworks has kept them in the spotlight in hyperspectral image (HSI) classification. Graph Convolutional Neural Networks (GCNs) can be used to compensate for the lack of spatial information in Convo...
Deep convolutional neural networks (DCNNs) have enjoyed much success in many applications, such as computer vision, automated medical diagnosis, autonomous systems, etc. Another application of DCNNs is for game strategies, where the deep neural netwo...
Traffic forecasting plays an important role in intelligent transportation systems. However, the prediction task is highly challenging due to the mixture of global and local spatiotemporal dependencies involved in traffic data. Existing graph neural n...
The neuroscience community has developed many convolutional neural networks (CNNs) for the early detection of Alzheimer’s disease (AD). Population graphs are thought of as non-linear structures that capture the relationships between individual...
Graph convolutional neural networks (GCNNs) have been successfully applied to a wide range of problems, including low-dimensional Euclidean structural domains representing images, videos, and speech and high-dimensional non-Euclidean domains, such as...
Convolutional neural networks (CNNs) and graph convolutional networks (GCNs) have made considerable advances in hyperspectral image (HSI) classification. However, most CNN-based methods learn features at a single-scale in HSI data, which may be insuf...
Graph convolutional networks (GCNs) show great potential in recommendation applications, as they have excellent performance in propagation node information propagation and capturing high-order connectivity in user-item interaction graphs. However, in...
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 g...
This paper explores the value of weak-ties in classifying academic literature with the use of graph convolutional neural networks. Our experiments look at the results of treating weak-ties as if they were strong-ties to determine if that assumption i...
Human action recognition is a computer vision challenge that involves identifying and classifying human movements and activities. The behavior of humans comprises movements of multiple body parts, and Graph Convolutional Networks (GCNs) have emerged...
Efficient learning of 3D shape representation from point cloud is one of the biggest requirements in 3D computer vision. In recent years, convolutional neural networks have achieved great success in 2D image representation learning. However, unlike i...
Graph convolutional networks (GCNs) have made significant progress in the skeletal action recognition task. However, the graphs constructed by these methods are too densely connected, and the same graphs are used repeatedly among channels. Redundant...
With the development of online interactive media platforms, a large amount of short text has appeared on the internet. Determining how to classify these short texts efficiently and accurately is of great significance. Graph neural networks can captur...
Heterogeneous graph embedding has become a hot topic in network embedding in recent years and has been widely used in lots of practical scenarios. However, most of the existing heterogeneous graph embedding methods cannot make full use of all the aux...
Graph convolutional networks (GCNs) have been successfully applied to learning tasks on graph-structured data. However, most traditional GCNs based on graph convolutions assume homophily in graphs, which leads to a poor performance when dealing with...
The selection of road networks is very important for cartographic generalization. Traditional artificial intelligence methods have improved selection efficiency but cannot fully extract the spatial features of road networks. However, current selectio...
Targeting the challenge of variable working conditions in bearing fault diagnosis, most of the fault diagnosis methods based on transfer learning focus on the transfer of knowledge, resulting in a poor diagnosis effect in the target domain. To solve...
Aspect-based sentiment classification aims at determining the corresponding sentiment of a particular aspect. Many sophisticated approaches, such as attention mechanisms and Graph Convolutional Networks, have been widely used to address this challeng...
The recent emergence of high-resolution Synthetic Aperture Radar (SAR) images leads to massive amounts of data. In order to segment these big remotely sensed data in an acceptable time frame, more and more segmentation algorithms based on deep learni...
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