Graph Convolutional Network Research Articles

A Research Approach to Port Information Security Link Prediction Based on HWA Algorithm

For the protection of information security, link prediction, as a basic problem of network science, has important application significance. However, most of the existing link prediction algorithms rely on the node information of the graph structure, which is not applicable in some graph structure data involving privacy. At the same time, most of the algorithms only consider the general graph structure and do not fully consider the high-order information in the graph. Because of this, this paper proposes an algorithm called hypergraph-based link prediction with self-attention (HWA) to solve the above problems. The algorithm can obtain hypergraphs without knowing the attribute information of hypergraph nodes and combines the graph convolutional network (GCN) framework to capture node feature information for link prediction. Experiments show that the HWA algorithm proposed in this paper, combined with the GCN framework, shows better link prediction performance than other graph-based neural network benchmark algorithms on eight real networks. This further verifies the validity and reliability of the model in this paper and provides new protection ideas and technical means for information security.

Building pattern recognition by using an edge-attention multi-head graph convolutional network

Building pattern recognition by using an edge-attention multi-head graph convolutional network

Traffic Flow Prediction Using Novel Spatial-Temporal Multi-Head Attention Graph Convolution Networks

For urban development, accurate traffic flow prediction is crucial. Traffic flow prediction data can be used for optimizing public transportation systems, reducing congestion, planning road networks, and improving overall city infrastructure management. Despite numerous spatiotemporal methods used to forecast traffic flow, information aggregation at each site neither produce optimal results, because of the use non-Euclidean distance data. For more accurate results of traffic flow prediction, we propose a spatial-temporal multi-head attention graph convolution network (STMAGCN). It extracts temporal features using temporal convolutional layers and enriches nodes with spatial attention and multi-head attention mechanisms. By combining them, we can extract space characteristics more effectively. We also introduce a novel graph structure construction method that employs graph-based transformations to convert non-connected data points into a comprehensive graph structure, facilitating the effective application of our STMAGCN. This approach improves the predictive efficiency and accuracy of our model by ensuring seamless integration of disparate data sources. Experimental results indicate that the preferential extraction of spatiotemporal data significantly enhances traffic flow prediction accuracy. Compared with the latest models, our performance on the California Department of Transportation Performance Measurement System (PeMS) PeMS04 and PeMS08 datasets shows a slight improvement, with an average increase of 16.8%. Additionally, the average improvement in time is approximately 445.95%.

Correction: Xu et al. Android Malware Detection Based on Behavioral-Level Features with Graph Convolutional Networks. Electronics 2023, 12, 4817

The journal has corrected the article “Android Malware Detection Based on Behavioral-Level Features with Graph Convolutional Networks” [...]

Physics-guided spatiotemporal approach for melt pool size prediction in laser powder bed fusion

ABSTRACT This study first proposes a physics-guided spatiotemporal graph. The melt pool temporal feature nodes and spatial feature nodes are extracted from melt pool images based on spatiotemporal correlations. These feature points are treated as graph nodes, and the edge weights in the graph are calculated based on an analytical heat transfer model. It supplements the physical information of accumulated heat. Based on this, the graph convolutional network (GCN) is used to extract spatiotemporal information of heat transfer, and the long short-term memory (LSTM) is used to capture the evolution patterns of the melt pool. Thus, a melt pool spatiotemporal feature extraction model is constructed for melt pool size prediction. The model achieves prediction errors within 12% for the melt pool area and 8.5% for the melt pool perimeter. Subsequently, interpretability analysis indicates that the physics-guided approach enhances the model's learning ability for the cumulative contribution of heat from neighboring tracks.

Combined graph convolutional networks with a multi-connection pattern to identify tremor-dominant Parkinson's disease and essential tremor with resting tremor.

Essential tremor with resting tremor (rET) and tremor-dominant Parkinson's disease (tPD) share many similar clinical symptoms, leading to frequent misdiagnoses. Functional connectivity (FC) matrix analysis derived from resting-state functional MRI (Rs-fMRI) offers a promising approach for early diagnosis and for exploring FC network pathogenesis in rET and tPD. However, methods relying solely on a single connection pattern may overlook the complementary roles of different connectivity patterns, resulting in reduced diagnostic differentiation. Therefore, we propose a multi-pattern connection Graph Convolutional Network (MCGCN) method to integrate information from various connection modes, distinguishing between rET and healthy controls (HC), tPD and HC, and rET and tPD. We constructed FC matrices using three different connectivity modes for each subject and used these as inputs to the MCGCN model for disease classification. The classification performance of the model was evaluated for each connectivity mode. Subsequently, gradient-weighted class activation mapping (Grad-CAM) was used to identify the most discriminative brain regions. The important brain regions identified were primarily distributed within cerebellar-motor and non-motor cortical networks. Compared with single-pattern GCN, our proposed MCGCN model demonstrated superior classification accuracy, underscoring the advantages of integrating multiple connectivity modes. Specifically, the model achieved an average accuracy of 88.0% for distinguishing rET from HC, 88.8% for rET from tPD, and 89.6% for tPD from HC. Our findings indicate that combining graph convolutional networks with multi-connection patterns can not only effectively discriminate between tPD, rET, and HC but also enhance our understanding of the functional network mechanisms underlying rET and tPD.

Point-of-interest recommendation based on the spatial-temporal graph

Purpose Point-of-interest (POI) recommendation techniques play a crucial role in mitigating information overload and delivering tailored services. To address limitations in conventional POI recommendation systems, constrained by sparse user-POI interactions and incomplete consideration of temporal dynamics, POI recommendation based on the spatial-temporal graph (STG-POI) is proposed. Design/methodology/approach Spatial-temporal sequence graphs from geographical locations and user interaction history data are constructed, which are used to mine spatial-temporal sequence information. Using the data filtered by the band-pass filter, graph neural networks with distance-awareness and sequence-awareness are applied to capture high-order spatial-temporal connections within diverse graph topologies. The model leverages contrastive learning for self-supervised disentanglement of graph representations, providing self-supervised signals for sequential and geographical intent perception, thereby achieving more precise POI personalization. Findings Compared to the baseline model GSTN, experiments on the Foursquare and Gowalla data sets reveal that STG-POI improves testing AUC by 2.0%, 2.1%, 2.0% and decreases logloss by 1.9%, 3.3%, 0.3%, respectively. These results indicate the model’s effectiveness in capturing spatial-temporal information, surpassing mainstream POI recommendation baseline models. Originality/value This approach constructs a dual graph from user interaction data, harnessing sequential and geographical information as self-supervised signals. It yields decoupled representations of these influences, offering a comprehensive insight into user behaviors and preferences within location-based social networks, thus enhancing recommendation accuracy and interpretability. This approach addresses the challenge in graph convolutional network where only rough and smooth features are conducive to recommendation by using band-pass filters to significantly reduce computational complexity, thereby enhancing recommendation speed by filtering out noise data that does not contribute to recommendation performance. Experimental results indicate that this model surpasses current mainstream approaches in POI recommendation tasks, effectively integrating both geographical and temporal features.

Optimization of the memristor fabrication based on graph convolutional network

Abstract In this article, a machine learning model for accurately predicting the performance of unknown memristors is constructed by employing a graph convolutional network approach. Thickness and elemental composition are used to transform memristors into graph-structured data. This model exhibits high accuracy and, based on extensive training with a certain type of memristor data, can be applied to novel memristors and give rapid predictions of the performance with only a small-batch sample reported in the literature, showing the potential for excellent transfer learning. This model is also applied to predict the performance of halide memristors, which have received less attention in current research, and it is indeed that a halide perovskite memristor with potential high switching ratio is predicted.

RCCNet: A Spatial-Temporal Neural Network Model for Logistics Delivery Timely Rate Prediction

In logistics service, the delivery timely rate is a key experience indicator, which is highly essential to the competitive advantage of express companies. Prediction on it enables intervention on couriers with low predicted results in advance, thus ensuring employee productivity and customer satisfaction. Currently, few related works focus on couriers’ level delivery timely rate prediction, and there are complex spatial correlations between couriers and road districts in the express scenario, which makes traditional real-time prediction approaches hard to utilize. To deal with this, we propose a deep spatial-temporal neural network, RCCNet to model spatial-temporal correlations. Specifically, we adopt Node2vec, which can encode the road network-based graph directly to capture spatial correlations between road districts. Further, we calculate couriers’ historical time-series similarity to build a graph and employ graph convolutional networks to capture the correlation between couriers. We also leverage historical sequential information with long short-term memory networks. We conduct experiments with real-world express datasets. Compared with other competitive baseline methods widely used in industry, the experiment results demonstrate its superior performance over multiple baselines.

Robust-DefReg: A Robust Coarse to Fine Non-rigid Point Cloud Registration Method based on Graph Convolutional Neural Networks

Abstract Point cloud registration is a critical process in computer vision and measurement science, aimed at determining transformations between corresponding sets of points for accurate spatial alignment. In particular, non-rigid registration involves estimating flexible transformations that map a source point cloud to a target point cloud, even under conditions of stretching, compression, or other complex deformations. This task becomes especially challenging when addressing measurement-specific issues like varying degrees of deformation, noise, and outliers, all of which can impact measurement accuracy and reliability.
This paper introduces Robust-DefReg, a novel method for non-rigid point cloud registration that applies graph convolutional networks (GCNNs) within a coarse-to-fine registration framework. This end-to-end pipeline harnesses global feature learning to establish robust correspondences and precise transformations, enabling high accuracy across different deformation scales and noise levels. A key contribution of Robust-DefReg is its demonstrated resilience to various challenges, such as substantial deformations, noise, and outliers, factors often underreported in existing registration literature.
In addition, we present SynBench, a comprehensive benchmark dataset specifically designed for evaluating non-rigid point cloud registration in realistic measurement scenarios. Unlike previous datasets, SynBench incorporates a range of challenges, making it a valuable tool for the fair assessment of registration methods in measurement applications. Experimental results on SynBench and additional datasets show that Robust-DefReg consistently outperforms state-of-the-art methods, offering higher registration accuracy and robustness, even with up to 45% outliers.
SynBench and the Robust-DefReg source code are publicly accessible for further research and development at https://doi.org/10.11588/data/R9IKCF and https://github.com/m-kinz/Robust-DefReg, respectively.

Dynamic Spatio-Temporal Hypergraph Convolutional Network for Traffic Flow Forecasting

Graph convolutional networks (GCN) are an important research method for intelligent transportation systems (ITS), but they also face the challenge of how to describe the complex spatio-temporal relationships between traffic objects (nodes) more effectively. Although most predictive models are designed based on graph convolutional structures and have achieved effective results, they have certain limitations in describing the high-order relationships between real data. The emergence of hypergraphs breaks this limitation. A dynamic spatio-temporal hypergraph convolutional network (DSTHGCN) model is proposed in this paper. It models the dynamic characteristics of traffic flow graph nodes and the hyperedge features of hypergraphs simultaneously, achieving collaborative convolution between graph convolution and hypergraph convolution (HGCN). On this basis, a hyperedge outlier removal mechanism (HOR) is introduced during the process of node information propagation to hyper-edges, effectively removing outliers and optimizing the hypergraph structure while reducing complexity. Through in-depth experimental analysis on real-world datasets, this method has better performance compared to other methods.

Trading Community Analysis of Countries’ Roll-On/Roll-Off Shipping Networks Using Fine-Grained Vessel Trajectory Data

Roll-on/roll-off vessels (RO/RO vessels) are playing an increasingly critical role in international automobile transport, facilitating the efficient movement of vehicles and heavy machinery across continents. Despite this growing significance, there is still limited research specifically focused on the RO/RO shipping network and its impact on global trade. This paper studies the global RO/RO shipping network using AIS data on RO/RO vessels collected from 2020 to 2023. We construct a method based on the complex network theory and the graph feature extraction method to quantitatively assess the features of the RO/RO shipping network. This method assesses the complexity, sparsity, homogeneity, modularity, and hierarchy of the RO/RO shipping network across various ports and countries and employs the graph convolutional neural network (GCN) model to extract network features for community detection. This process enables the identification of port clusters that are frequently linked to RO/RO vessels, as well as regional transport modes. The paper’s findings support these conclusions: (1) From 2020 to 2023, the number of nodes in the RO/RO shipping network increased by 22%, primarily concentrated in African countries. The RO/RO shipping network underwent restructuring after the pandemic, with major complex network parameters showing an upward trend. (2) The RO/RO shipping network is complex, with a stable graph density of 0.106 from 2020 to 2023. The average degree increased by 7% to 4.224. Modularity decreased by 6.5% from 0.431 in 2022 to 0.403, while the hierarchy coefficient rose to 0.575, suggesting that post-pandemic, community routes have become more diverse, reflecting the reconstruction and maturation of the overall network. (3) The model yielded a silhouette coefficient of 0.548 and a Davies–Bouldin index of 0.559 using an improved automatic feature extraction method. In comparison between 2020 and 2023, the changes in the two indicators are small. This shows that GINs can effectively extract network features and give us results that we can understand for community detection. (4) In 2023, key communities divide the RO/RO shipping network, with one community handling 39% of global routes (primarily Europe–Asia), another community handling 23% (serving Asia–Pacific, Africa, and the Middle East), and a third community managing 38% (linking Asia, Europe, and South America).

Predicting travel demand of a bike sharing system using graph convolutional neural networks

Predicting travel demand of a bike sharing system using graph convolutional neural networks

Hierarchical multi-label classification model for science and technology news based on heterogeneous graph semantic enhancement

In the context of high-quality economic development, technological innovation has emerged as a fundamental driver of socio-economic progress. The consequent proliferation of science and technology news, which acts as a vital medium for disseminating technological advancements and policy changes, has attracted considerable attention from technology management agencies and innovation organizations. Nevertheless, online science and technology news has historically exhibited characteristics such as limited scale, disorderliness, and multi-dimensionality, which is extremely inconvenient for users of deep application. While single-label classification techniques can effectively categorize textual information, they face challenges in leading science and technology news classification due to a lack of a hierarchical knowledge framework and insufficient capacity to reveal knowledge integration features. This study proposes a hierarchical multi-label classification model for science and technology news, enhanced by heterogeneous graph semantics. The model captures multi-dimensional themes and hierarchical structural features within science and technology news through a hierarchical transmission module. It integrates graph convolutional networks to extract node information and hierarchical relationships from heterogeneous graphs, while also incorporating prior knowledge from domain knowledge graphs to address data scarcity. This approach enhances the understanding and classification capabilities of the semantics of science and technology news. Experimental results demonstrate that the model achieves precision, recall, and F1 scores of 84.21%, 88.89%, and 86.49%, respectively, significantly surpassing baseline models. This research presents an innovative solution for hierarchical multi-label classification tasks, demonstrating significant application potential in addressing data scarcity and complex thematic classification challenges.

A survey of graph convolutional networks (GCNs) in FPGA-based accelerators

AbstractThis survey overviews recent Graph Convolutional Networks (GCN) advancements, highlighting their growing significance across various tasks and applications. It underscores the need for efficient hardware architectures to support the widespread adoption and development of GCNs, particularly focusing on platforms like FPGAs known for their performance and energy efficiency. This survey also outlines the challenges in deploying GCNs on hardware accelerators and discusses recent efforts to enhance efficiency. It encompasses a detailed review of the mathematical background of GCNs behind inference and training, a comprehensive review of recent works and architectures, and a discussion on performance considerations and future directions.

Spatio-temporal attention based collaborative local–global learning for traffic flow prediction

Traffic flow prediction is crucial for intelligent transportation systems (ITS), providing valuable insights for traffic control, route planning, and operation management. Existing work often separately models the spatial and temporal dependencies and primarily relies on predefined graphs to represent spatio-temporal dependencies, neglecting the traffic dynamics caused by unexpected events and the global relationships among road segments. Unlike previous models that primarily focus on local feature extraction, we propose a novel collaborative local–global learning model (LOGO) that employs spatio-temporal attention (STA) and graph convolutional networks (GCN). Specifically, LOGO simultaneously extracts hidden traffic features from both local and global perspectives. In local feature extraction, a novel STA is devised to directly attend to spatio-temporal coupling interdependencies instead of separately modeling temporal and spatial dependencies, and to capture in-depth spatio-temporal traffic context with an adaptive graph focusing on the dynamics in traffic flow. In global feature extraction, a global correlation matrix is constructed and GCNs are utilized to propagate messages on the obtained matrix to achieve interactions between both adjacent and similar road segments. Finally, the obtained local and global features are concatenated and fed into a gated aggregation to forecast future traffic flow. Extensive experiments on four real-world traffic datasets sourced from the Caltrans Performance Measurement System (PEMS03, PEMS04, PEMS07, and PEMS08) demonstrate the effectiveness of our proposed model. LOGO achieves the best performance over 18 state-of-the-art baselines and the best prediction performance with the highest improvement of 6.06% on the PEMS07 dataset. Additionally, two real-world case studies further substantiate the robustness and interpretability of LOGO.

MLP Feature Extraction from Coordinates for Building Footprint Simplification using Graph Convolutional Networks

MLP Feature Extraction from Coordinates for Building Footprint Simplification using Graph Convolutional Networks

PYRAMID CHANNEL-BASED FEATURE ATTENTION NETWORK WITH ENSEMBLE LEARNING-BASED UAV IMAGE CLASSIFICATION ON IoT-ASSISTED REMOTE SENSING ENVIRONMENT

Advances in Unmanned Aerial Vehicles (UAVs), otherwise recognized as drones, have tremendous promise in improving the wide-ranging applications of the Internet of Things (IoT). UAV image classification using deep learning (DL) is an amalgamation to modernize data analysis, collection, and decision-making in a variety of sectors. IoT devices collect information in real time, while remote sensing captures data afar without direct contact. UAVs equipped with sensors offer high-quality images for classification tasks. DL techniques, especially the convolutional neural networks (CNNs), analyze data streams, extracting complicated features for the accurate classification of objects or environmental features. This synergy enables applications including urban planning and precision agriculture, fostering smarter disaster response, decision support systems, and efficient resource management. This paper introduces a novel Pyramid Channel-based Feature Attention Network with an Ensemble Learning-based UAV Image Classification (PCFAN-ELUAVIC) technique in an assisted remote sensing environment. The PCFAN-ELUAVIC technique begins with the contrast enhancement of the UAV images using the CLAHE technique. Following that, the feature vectors are derived by the use of the PCFAN model. Meanwhile, the hyperparameter tuning procedure is executed by the inclusion of a vortex search algorithm (VSA). For image classification, the PCFAN-ELUAVIC technique comprises an ensemble of three classifiers like long short-term memory (LSTM), graph convolutional networks (GCNs), and Hermite neural network (HNN). To exhibit the improved detection results of the PCFAN-ELUAVIC system, an extensive range of experiments are carried out. The experimental values confirmed the enhanced performance of the PCFAN-ELUAVIC model when compared to other techniques.

Prediction of electric vehicle charging demand using enhanced gated recurrent units with RKOA based graph convolutional network

Prediction of electric vehicle charging demand using enhanced gated recurrent units with RKOA based graph convolutional network

Comparative Analysis of Deep Neural Networks and Graph Convolutional Networks for Road Surface Condition Prediction

Road maintenance is essential for supporting road safety and user comfort. Developing predictive models for road surface conditions enables highway agencies to optimize maintenance planning and strategies. The international roughness index (IRI) is widely used as a standard for evaluating road surface quality. This study compares the performance of deep neural networks (DNNs) and graph convolutional networks (GCNs) in predicting IRI values. A unique aspect of this research is the inclusion of additional predictor features, such as the type and timing of recent roadwork, hypothesized to affect IRI values. Findings indicate that, overall, the DNN model performs similarly to the GCN model across the entire highway network. Given the predominantly linear structure of national highways and their limited connectivity, the dataset exhibits a low beta index, ranging from 0.5 to 0.75. Additionally, gaps in IRI data collection and discontinuities in certain highway segments present challenges for modeling spatial dependencies. The performance of DNN and GCN models was assessed across the network, with results indicating that DNN outperforms GCN when highway networks are sparsely connected. This research underscores the suitability of DNN for low-connectivity networks like highways, while also highlighting the potential of GCNs in more densely connected settings.