Spatial Graph Research Articles

On the Use of Spatial Graphs for Performance Degradation Root-Cause Analysis Toward Self-Healing Mobile Networks

On the Use of Spatial Graphs for Performance Degradation Root-Cause Analysis Toward Self-Healing Mobile Networks

Semi-Supervised Hyperspectral Image Classification Based on Multiscale Spectral–Spatial Graph Attention Network

Semi-Supervised Hyperspectral Image Classification Based on Multiscale Spectral–Spatial Graph Attention Network

Fuzzy State-Driven Cross-Time Spatial Dependence Learning for Multivariate Time-Series Anomaly Detection.

Cross-time spatial dependence (i.e., the interaction between different variables at different time points) is indispensable for detecting anomalies in multivariate time series, as certain anomalies may have time delays in their propagation from one variable to another. However, accurately capturing cross-time spatial dependence remains a challenge. Specifically, real-world time series usually exhibits complex and incomprehensible evolutions that may be compounded by multiple temporal states (i.e., temporal patterns, such as rising, fluctuating, and peak). These temporal states mix and overlap with each other and exhibit dynamic and heterogeneous evolution laws in different time series, making the cross-time spatial dependence extremely intricate and mutable. Therefore, a cross-time spatial graph network with fuzzy embedding is proposed to disentangle latent and mixing temporal states and exploit it to meticulously learn cross-time spatial dependence. First, considering that temporal states are diversiform and their mixing modes are unknown, we introduce a fuzzy state set to uniformly characterize potential temporal states and adaptively generate corresponding membership degrees to depict how these states mix. Further, we propose a cross-time spatial graph, quantifying similarities among fuzzy states and sensing their dynamic evolutions, to flexibly learn mutable cross-time spatial dependence. Finally, we design state diversity and temporal proximity constraints to ensure the differences among fuzzy states and the evolution continuity of fuzzy states. Experiments on real-world datasets show that the proposed model outperforms the state-of-the-art models.

Spatio-Temporal Joint Graph Convolutional Networks for Traffic Forecasting

Recent studies have shifted their focus towards formulating traffic forecasting as a spatio-temporal graph modeling problem. Typically, they constructed a static spatial graph at each time step and then connected each node with itself between adjacent time steps to create a spatio-temporal graph. However, this approach failed to explicitly reflect the correlations between different nodes at different time steps, thus limiting the learning capability of graph neural networks. Additionally, those models overlooked the dynamic spatio-temporal correlations among nodes by using the same adjacency matrix across different time steps. To address these limitations, we propose a novel approach called Spatio-Temporal Joint Graph Convolutional Networks (STJGCN) for accurate traffic forecasting on road networks over multiple future time steps. Specifically, our method encompasses the construction of both pre-defined and adaptive spatio-temporal joint graphs (STJGs) between any two time steps, which represent comprehensive and dynamic spatio-temporal correlations. We further introduce dilated causal spatio-temporal joint graph convolution layers on the STJG to capture spatio-temporal dependencies from distinct perspectives with multiple ranges. To aggregate information from different ranges, we propose a multi-range attention mechanism. Finally, we evaluate our approach on five public traffic datasets and experimental results demonstrate that STJGCN is not only computationally efficient but also outperforms 11 state-of-the-art baseline methods.

University Campus as a Complex Pedestrian Dynamic Network: A Case Study of Walkability Patterns at Texas Tech University

Statistical mechanics of walks defined on the spatial graphs of the city of Lubbock (10,421 nodes) and the Texas Tech University (TTU) campus pedestrian network (1466 nodes) are used for evaluating structural isolation and the integration of graph nodes, assessing their accessibility and navigability in the graph, and predicting possible graph structural modifications driving the campus evolution. We present the betweenness and closeness maps of the campus, the first passage times to the different campus areas by isotropic and anisotropic random walks, as well as the first passage times under the conditions of traffic noise. We further show the isolation and integration indices of all areas on the campus, as well as their navigability and strive scores, and energy and fugacity scores. The TTU university campus, a large pedestrian zone located close to the historical city center of Lubbock, mediates between the historical city going downhill and its runaway sprawling body.

Hybrid Convolutional Neural Network Approaches for Recognizing Collaborative Actions in Human–Robot Assembly Tasks

In the context of sustainable manufacturing, efficient collaboration between humans and machines is crucial for improving assembly quality and efficiency. However, traditional methods for action recognition and human–robot collaborative assembly often face challenges such as low efficiency, low accuracy, and poor robustness. To solve such problems, this paper proposes an assembly action-recognition method based on a hybrid convolutional neural network. Firstly, an assembly action-recognition model is proposed using skeletal sequences and a hybrid convolutional neural network model combining Spatial Temporal Graph Convolutional Networks (ST-GCNs) and One-Dimensional Convolutional Neural Networks (1DCNNs) to sense and recognize human behavior actions during the assembly process. This model combines the joint spatial relationship and temporal information extraction ability of the ST-GCN model with the temporal feature extraction ability of the 1DCNN model. By incorporating Batch Normalization (BN) layers and Dropout layers, the generalization performance of the model is enhanced. Secondly, the model is validated on a self-constructed dataset of assembly actions, and the results show that the recognition accuracy of the model can reach 91.7%, demonstrating its superiority. Finally, a digital workshop application system based on digital twins is developed. To test the effectiveness of the proposed method, three sets of control experiments were designed to evaluate both objective and subjective aspects and verify the feasibility of the method presented in this paper. Compared with traditional assembly systems, the proposed method optimizes the recognition of human–robot collaborative assembly actions and applies them to intelligent control systems using digital-twin technology. This intelligent assembly method improves assembly efficiency and saves assembly time. It enables efficient and sustainable collaboration between humans and robots in assembly, leading to a positive and sustainable impact on the manufacturing industry.

Parameter estimation for network-organized Turing system based on convolution neural networks

Turing dynamics mainly focuses on non-homogeneous self-adaptive spatial patterns of reaction diffusion systems in a continuous space. If defining the diffusion environment as network structure, then network patterns are available under Turing instability conditions. In allusion to parameter estimation for network Turing-instable systems, this paper proposes a new recognition method using artificial neural networks. When diffusion occurs on quadrilateral lattice networks, a deep convolutional neural network (CNN) is built to invert the unknown parameters. The results on training set and validation set indicate that the CNN model exhibits great robustness without overfitting and gradient explosion. Meanwhile, the prediction performance on two test sets is excellent, with average relative errors of the estimators ending up at 0.68% and 1.04%, respectively. When diffusion occurs on non-regular networks, a spatial domain graph convolutional network (GCN) is established, which is slightly less robust than the CNN. The average relative error of the estimators on both test sets ends up between 1.1% and 2.8%, which is still in the ideal range.

Topology-based optimization of handcuff graphs on 3D lattice

In the field of macromolecular chemistry, handcuff-shaped catenanes and pretzelanes have a conformation consisting of two distinct loops and an edge connecting them. In spatial graph theory, this shape is referred to as a handcuff graph. One topological aspect of interest in these molecular structures involves determining the minimal number of monomers required to create them. In this paper, we focus on a handcuff graph situated in the cubic lattice, which we refer to as a lattice handcuff graph. We explicitly verify that constructing a lattice handcuff graph requires at least 14 lattice sticks, except for the two handcuff graphs: the trivial handcuff graph and the Hopf-linked handcuff graph. Mainly we employ the properly leveled lattice conformation argument, which was developed by the authors to find the lattice stick number of knot-shaped and link-shaped molecules.

Vehicle Trajectory Prediction Based on Graph Convolutional Networks in Connected Vehicle Environment

Vehicle trajectory prediction is an important research basis for the decision making and path planning of the intelligent and connected vehicle. In the connected vehicle environment, vehicles share information and drive cooperatively, and the intelligent and connected vehicles are able to obtain more accurate and rich perception information, which provides a data basis for accurate prediction of vehicle trajectories. However, attaining accurate and effective vehicle trajectory predictions poses technical challenges due to insufficient extraction of vehicular spatial–temporal interaction features. In this paper, we propose a vehicle trajectory prediction model based on graph convolutional neural network (GCN) in a connected vehicle environment. Specifically, using the driving scene information obtained by the intelligent and connected vehicle, the spatial graph and temporal graph are constructed based on the spatial interaction coefficient (SIC) and self-attention mechanism, respectively. Then, the graph data are entered into the interaction extraction module, and the spatial interaction features and temporal interaction features are extracted separately using the graph convolutional networks, which are fused to obtain the spatial–temporal interaction information. Finally, the interaction features are learned based on the convolutional neural networks to output the future trajectory information of all vehicles in the scene by one forward operation rather than a step-by-step process. The ablation experiment results show that the method proposed in this study to model the spatiotemporal interaction among vehicles based on SIC and self-attention mechanism reduces the prediction error by 5% and 12%, respectively. The results from the model comparison experiment show that the proposed method engenders an 8% improvement in prediction accuracy over the state-of-the-art solution, providing technical and theoretical support for trajectory prediction research of intelligent and connected vehicles.

Deep learning of cell spatial organizations identifies clinically relevant insights in tissue images

Recent advancements in tissue imaging techniques have facilitated the visualization and identification of various cell types within physiological and pathological contexts. Despite the emergence of cell-cell interaction studies, there is a lack of methods for evaluating individual spatial interactions. In this study, we introduce Ceograph, a cell spatial organization-based graph convolutional network designed to analyze cell spatial organization (for example,. the cell spatial distribution, morphology, proximity, and interactions) derived from pathology images. Ceograph identifies key cell spatial organization features by accurately predicting their influence on patient clinical outcomes. In patients with oral potentially malignant disorders, our model highlights reduced structural concordance and increased closeness in epithelial substrata as driving features for an elevated risk of malignant transformation. In lung cancer patients, Ceograph detects elongated tumor nuclei and diminished stroma-stroma closeness as biomarkers for insensitivity to EGFR tyrosine kinase inhibitors. With its potential to predict various clinical outcomes, Ceograph offers a deeper understanding of biological processes and supports the development of personalized therapeutic strategies.

Using complex networks and multiple artificial intelligence algorithms for table tennis match action recognition and technical-tactical analysis

This study developed two action recognition models using the YOLOv8-Alphapose two-stream spatial temporal graph convolutional networks (2s-STGCN), and the networks were used to recognize technical actions in table tennis. This study proposed a novel framework that merges dynamic and static complex network analysis with a community detection algorithm aimed at evaluating table tennis players' techniques, tactical patterns and styles. Two datasets that contain 8015 high-definition action videos of 37 elite players were constructed: a front-facing player technical action dataset (4154 videos) and a backwards-facing player technical action dataset (3861 videos). The results showed that YOLOv8-Alphapose-2s-STGCN achieved better recognition performance than seven other YOLOv8-Alphapose-based artificial intelligence algorithms (transformer, BiGRU, BiLSTM, GRU, LSTM, TCN and RNN algorithms) on both datasets and exhibited robust performance in practical applications. In the case study, multiple indicators were used to measure the importance of nodes (players' techniques) within the serving and receiving networks and within the two-round (winning and losing) networks. Dynamic complex network analysis was adopted to evaluate tactical styles and patterns. Furthermore, this study examined whether players and their opponents exhibit variability or similarity in their tactical patterns, focusing on the player networks and the two-round winning and losing networks. By integrating action recognition with process-focused match analysis, this study explored an innovative and comprehensive way to analyse matches, with implications for the performance analysis of table tennis players and players in related racket sports.

Multi-view graph representation learning for hyperspectral image classification with spectral–spatial graph neural networks

Multi-view graph representation learning for hyperspectral image classification with spectral–spatial graph neural networks

Adaptive Spatio-Temporal Graph Learning for Bus Station Profiling

Understanding and managing public transportation systems require capturing complex spatio-temporal correlations within datasets. Existing studies often use predefined graphs in graph learning frameworks, neglecting shifted spatial and long-term temporal correlations, which are crucial in practical applications. To address these problems, we propose a novel bus station profiling framework to automatically infer the spatio-temporal correlations and capture the shifted spatial and long-term temporal correlations in the public transportation dataset. The proposed framework adopts and advances the graph learning structure through the following innovative ideas: (1) Designing an adaptive graph learning mechanism to capture the interactions between spatio-temporal correlations rather than relying on pre-defined graphs; (2) Modeling shifted correlation in shifted spatial graphs to learn fine-grained spatio-temporal features; (3) Employing self-attention mechanism to learn the long-term temporal correlations preserved in public transportation data. We conduct extensive experiments on three real-world datasets and exploit the learned profiles of stations for the station passenger flow prediction task. Experimental results demonstrate that the proposed framework outperforms all baselines under different settings and can produce meaningful bus station profiles.

Reinforced Imitative Graph Learning for Mobile User Profiling

Mobile user profiling refers to the efforts of extracting users’ characteristics from mobile activities. In order to capture the dynamic varying of user characteristics for generating effective user profiling, we propose an imitation-based mobile user profiling framework. Considering the objective of teaching an autonomous agent to imitate user mobility based on the user’s profile, the user profile is the most accurate when the agent can perfectly mimic the user behavior patterns. The profiling framework is formulated into a reinforcement learning task, where an agent is a next-visit planner, an action is a POI that a user will visit next, and the state of the environment is a fused representation of a user and spatial entities. An event in which a user visits a POI will construct a new state, which helps the agent predict users’ mobility more accurately. In the framework, we introduce a spatial Knowledge Graph (KG) to characterize the semantics of user visits over connected spatial entities. Additionally, we develop a mutual-updating strategy to quantify the state that evolves over time. Along these lines, we develop a reinforcement imitative graph learning framework for mobile user profiling. Finally, we conduct extensive experiments to demonstrate the superiority of our approach.

Edge and Skeleton Guidance Network for Salient Object Detection in Optical Remote Sensing Images

The salient object detection of optical remote sensing images (ORSI-SOD) is an important research direction in ORSI processing, which has achieved promising results in the last few years. Many recent works heavily rely on feature learning of regions for the improvement of the detection accuracy, while neglecting the concrete role of edge and skeleton information in the calculation. In this work, we propose a two-stage edge and skeleton guidance network (ESGNet) for ORSI-SOD in a coarse-to-fine way, and further demonstrate that the fused features of edge and skeleton are essential for ORSI-SOD. In the first stage, we construct the spatial graph attention (SGA) module for saliency features to generate an initial saliency map, and apply the spatial self-optimization (SSO) to enhance edge and skeleton features. The multi-level interactive fusion (MIF) module is used for the adequate integration of edge and skeleton features into saliency features. In the second stage, with the aim to accomplish better prediction of salient object localization and shape, the feature enhancement integration operation is introduced to recover object details from the learned edge and skeleton features. Extensive experiments on three public ORSI-SOD datasets demonstrate that our ESGNet achieves competitive performance with the state-of-the-art methods and also confirms the importance of edge and skeleton information for ORSI-SOD. Meanwhile, generalizability experiments on natural image datasets show that our method is competent for many types of SOD tasks. The code and results of our method are available at https://github.com/aoao0206/ESGNet.

Examining the urban regeneration of public cultural space using multi-scale geospatial data: a case study of the historic district in Jinan, China

Introduction: Historical-cultural landscapes and urbanscapes integrate regional culture, collective memory, urban patterns, living habits, and other related historical and spatial information, and are important places for expressing the values of urban space. This study analyzes cultural landscapes and urbanscapes from the perspective of spatial values, taking the Baihuazhou historic and cultural district in Jinan as an example to analyze the spatial identification of public cultural space. The significance of this research lies in examining the urban regeneration of public cultural space using multi-scale geospatial data from both an objective and subjective perspective. This methdology is based on a large body of previous literature in relation to cultural heritage, and draws on a comprehensive understanding of the field.Methods: This study applies spatial syntax, visual graph, and survey analyses, focusing on urban spatial patterns, spatial culture, and spatial memory as heritage values.Results: The analysis results reveal that historic districts generally do not have good accessibility, predominantly because of the disorganized texture of the buildings and absence of spatial consistency.Discussion: There should be improved dynamic spatial identification and accessibility in older urban environments and enhancement of local spatial identification memory by increasing sightline accessibility. Based on consideration of values, corresponding suggestions are made from the three perspectives of urban space, cultural landscapes, and local collective memories to provide a reference for the protection of the Baihuazhou historic district and promote the preservation and development of similar heritage spaces.

Multi-task Contexture Learning Network for automated vertebrae segmentation and tumor diagnosis from MRI

Automated vertebrae segmentation and tumor diagnosis from magnetic resonance imaging (MRI) scans play a crucial role in early detection of spinal tumors. However, the task is challenging due to the variability in fields of view across MRI images and the diverse appearances of tumors. To address these challenges, we propose a novel approach, the Multi-task Contexture Learning Network (MCLN), which concurrently performs accurate vertebrae segmentation and precise identification of tumors. Specifically, MCLN first constructs a spatial pyramid graph convolutional network and a label attention network to effectively capture long-range contextual information and generate a precise segmentation mask. Then, MCLN incorporates the input MRI image and the segmentation mask to recognize vertebral tumors utilizing a mixed kernel multi-loss network. We evaluate our proposed MCLN on a challenging dataset consisting of 600 MRI images, achieving impressive results with a mean Dice coefficient of 90.03% for vertebrae segmentation and a mean accuracy of 95.11% for tumor diagnosis. These results demonstrate the remarkable performance of MCLN in addressing automated vertebrae segmentation and tumor diagnosis from MRI.

Spatio-temporal multi-graph transformer network for joint prediction of multiple vessel trajectories

The vessel trajectory prediction plays a vital role in guaranteeing traffic safety for unmanned surface vehicles and autonomous surface vessels. By leveraging advanced satellite communication technology, AIS provides massive vessel trajectories, significantly enhancing maritime safety and decision-making. This research proposes a spatio-temporal multi-graph transformer network (ST-MGT), aiming to predict multiple vessel trajectories simultaneously. This innovative model amalgamates the capabilities of graph convolutional networks (GCNs) and transformer models to proficiently address the spatial and temporal interactions amongst vessels. The ST-MGT is comprised of three crucial layers. The temporal transformer layer employs sophisticated temporal transformer and memory mechanisms to discern the intricate temporal correlations between vessel movements. The spatial multi-graph transformer layer constructs a comprehensive multi-graph representation to illuminate spatial correlations between vessels. It incorporates a spatial graph convolutional network and transformer to meticulously understand and interpret the diverse and complex spatial interactions amongst varying vessels. Lastly, the ξ-Regularized LSTM (RegLSTM) layer is implemented for predicting vessel trajectories accurately, based on the unraveled spatio-temporal patterns. Extensive and meticulous experiments reveal that our proposed ST-MGT method transcends other state-of-the-art prediction models in robustness and accuracy. The model’s capability to facilitate multi-vessel and multi-step prediction showcases its immense potential and adaptability in intricate and multifaceted navigation environments, underscoring its practical applicability and significance in enhancing maritime navigational safety.

DanceTrend: An Integration Framework of Video-Based Body Action Recognition and Color Space Features for Dance Popularity Prediction

Background: With the rise of user-generated content (UGC) platforms, we are witnessing an unprecedented surge in data. Among various content types, dance videos have emerged as a potent medium for artistic and emotional expression in the Web 2.0 era. Such videos have increasingly become a significant means for users to captivate audiences and amplify their online influence. Given this, predicting the popularity of dance videos on UGC platforms has drawn significant attention. Methods: This study postulates that body movement features play a pivotal role in determining the future popularity of dance videos. To test this hypothesis, we design a robust prediction framework DanceTrend to integrate the body movement features with color space information for dance popularity prediction. We utilize the jazz dance videos from the comprehensive AIST++ street dance dataset and segment each dance routine video into individual movements. AlphaPose was chosen as the human posture detection algorithm to help us obtain human motion features from the videos. Then, the ST-GCN (Spatial Temporal Graph Convolutional Network) is harnessed to train the movement classification models. These pre-trained ST-GCN models are applied to extract body movement features from our curated Bilibili dance video dataset. Alongside these body movement features, we integrate color space attributes and user metadata for the final dance popularity prediction task. Results: The experimental results endorse our initial hypothesis that the body movement features significantly influence the future popularity of dance videos. A comprehensive evaluation of various feature fusion strategies and diverse classifiers discern that a pre–post fusion hybrid strategy coupled with the XGBoost classifier yields the most optimal outcomes for our dataset.

MDSSN: An end-to-end deep network on triangle mesh parameterization

Mesh data plays a crucial role in a wide range of applications worldwide within the field of 3D computer vision. In contrast to traditional Euclidean space arrangements, meshes encompass spatial information, including edges, faces, angles, and graph structures that embed face information within the mesh data. Nevertheless, conventional deep learning frameworks such as convolutional neural networks (CNNs) have struggled to achieve significant advancements in handling meshes. This paper proposes a simple mesh computation framework called Mesh Decomposition Second-order Sobolev Network (MDSSN) to model triangle meshes and represent their shape. The construction of face-based Riemannian and edge-based Riemannian graphs is motivated by the remarkable representation capabilities of edges and faces in mesh data, achieved through a graph composition mechanism. Furthermore, we design end-to-end operators, including the Sobolev-filter block, pooling, and unpooling blocks, which draw inspiration from traditional deep learning frameworks for modeling the mesh structure. Importantly, the design of the mesh-filter block incorporates the second-order Sobolev metric. To address common challenges in mesh classification and segmentation tasks, we have designed dedicated classification and segmentation modules. We utilize a joint loss function that integrates losses from adjacent faces and categories. We evaluate the performance of the MDSSN in mesh classification and segmentation tasks, focusing on its ability to represent 3D shapes. Experimental results demonstrate that MDSSN achieves superior performance compared to other state-of-the-art approaches.