Hyperspectral image classification has been acknowledged as the fundamental and challenging task of hyperspectral data processing. The abundance of spectral and spatial information has provided great opportunities to effectively characterize and iden...
Hyperspectral image (HSI) data classification often faces the problem of the scarcity of labeled samples, which is considered to be one of the major challenges in the field of remote sensing. Although active deep networks have been successfully appli...
As a crucial vehicle for green technological innovation, cooperative networks significantly promote resource integration and knowledge sharing. Yet, their dynamic evolution and micro-mechanism remain underexplored. Drawing on data from the joint appl...
Three-dimensional multi-object tracking (3D MOT) plays a vital role in enabling reliable perception for LiDAR-based autonomous systems. However, LiDAR measurements often exhibit sparsity, occlusion, and sensor noise that lead to uncertainty and insta...
Safe-separation-and-collision-avoidance unmanned aerial vehicle (UAV) swarms are increasingly used for inspection, emergency response, environmental monitoring, and search-and-rescue support in cluttered airspace where communication links may be dela...
Driven by comparative returns, non-grain use of cultivated land (NGUCL) has intensified, posing risks to food security. This study approaches the problem by employing a risk transfer valuation framework, integrating a multi-regional input–outpu...
This paper uses the multi-regional input–output model to measure China’s interprovincial embodied carbon transfer and constructs an interprovincial network; then, the temporal exponential random graph model is applied to analyze the spati...
Microbiota dysbiosis is closely associated with a wide range of human diseases, yet wet-lab validation remains costly and time-consuming. Therefore, this study aims to develop an efficient framework for predicting potential microbe–disease asso...
Ship route planning is a crucial activity that must consider not only the safety of the ship but also the safe passage of nearby ships in the same space and time. This study aims to provide general route-planning guidance to shipping traffic by impro...
Network-group targets are a set of objectives that adhere to a shared communication protocol, perform common tasks, and exhibit relatively coordinated movements. Typically, network-group targets emit radar and communication signals. However, they oft...
Promoting the sustainable development of virtual water trade is of great significance to safeguarding China’s water resource security and balanced regional economic growth. This study analyzes the virtual water trade network among 31 Chinese pr...
Loss of production due to local water scarcity, i.e., Local Water Scarcity Risk (LWSR), is transferred downstream through international supply chains to distant economies, causing potential economic losses to countries and sectors that do not directl...
This paper presents an efficient algorithm for matching subgraph queries in a multi-graph based on features-based indexing techniques. The KD-tree data structure represents these nodes’ features, while the set-trie index data structure represen...
Timely and accurate traffic flow prediction is crucial for stabilizing road conditions, reducing environmental pollution, and mitigating economic losses. While current graph convolution methods have achieved certain results, they do not fully leverag...
A continuous-time network evolution model is considered. The evolution of the network is based on 2- and 3-interactions. 2-interactions are described by edges, and 3-interactions are described by triangles. The evolution of the edges and triangles is...
Cancer is a complex and evolutionary disease mainly driven by the accumulation of genetic variations in genes. Identifying cancer driver genes is important. However, most related studies have focused on the population level. Cancer is a disease with...
Accurate identification of road network intersections is essential for urban planning, autonomous navigation, and traffic safety analysis. However, standard approaches relying on local geometric attributes often overlook essential topological informa...
Quantum secure multi-party summation (QSMS) is a fundamental problem in quantum secure multi-party computation (QSMC), wherein multiple parties compute the sum of their data without revealing them. This paper proposes a novel QSMS protocol based on g...
In this paper, the use of the construction of random processes on graphs allows us to expand the models of the theory of queuing and reliability by constructing. These problems are important because the emphasis on the legal component largely determi...
Truck cranes, which are crucial construction equipment, need to maintain good operational performance to ensure safe use. However, the complex and ever-changing working conditions they face often make it challenging to test their performance effectiv...
Change detection (CD) in remote sensing imagery has found broad applications in ecosystem service assessment, disaster evaluation, urban planning, land utilization, etc. In this paper, we propose a novel graph model-based method for synthetic apertur...
Multi-modal knowledge graph completion (MMKGC) aims to complete knowledge graphs by integrating structural information with multi-modal (e.g., visual, textual, and numerical) features and leveraging cross-modal reasoning within a unified semantic spa...
Blockchain networks have become a major target for fraudulent activities, including phishing and scamming attacks, due to the irreversible and pseudonymous nature of cryptocurrency transactions. Existing Ethereum fraud detection studies commonly form...
Anchor-based bipartite graph methods provide scalable solutions for multi-view clustering, but most of them construct graphs in the original feature space, where high dimensionality distorts the proximity between samples and anchors and degrades grap...
Multi-hop knowledge graph question answering (KGQA) faces practical deployment hurdles: large language models suffer from high computational overhead and hallucination risks, while graph-based methods rely on entity-centric subgraphs that adapt poorl...
Short-term airport traffic forecasting faces a structural gap: temporal ensemble models ignore route-network dependencies that shape hub operations, while deep graph neural networks require synchronised multi-airport operational data streams unavaila...
Constructing a knowledge graph of geological hazards literature can facilitate the reuse of geological hazards literature and provide a reference for geological hazard governance. Named entity recognition (NER), as a core technology for constructing...
The popularity of information technology has given rise to a growing interest in smart education and has provided the possibility of combining online and offline education. Knowledge graphs, an effective technology for knowledge representation and ma...
To address the fragmentation and weak correlation of knowledge in the design method domain, this paper proposes a framework for constructing a knowledge graph driven by multi-source heterogeneous data. The process involves collecting multi-source het...
Classroom acoustic quality is strongly influenced by spatial configuration, source–receiver relationships, and node-specific acoustic properties, making rapid and interpretable prediction valuable for building-acoustic assessment and design. Th...
Diseases and pests have a significant impact on rice production, affecting both yield and quality. Therefore, their effective management and control are crucial for successful rice cultivation. However, current research based on rice diseases and pes...
Skeletal muscle atrophy is a common condition in aging, diabetes, and in long duration spaceflights due to microgravity. This article investigates multi-modal gene disease and disease drug networks via link prediction algorithms to select drugs for r...
Methods to learn informative representations of road networks constitute an important prerequisite to solve multiple traffic analysis tasks with data-driven models. Most existing studies are only developed from a topology structure or traffic attribu...
Knowledge-graph-enhanced recommendation leverages external knowledge to characterize item semantics and alleviate the limitations of representation learning under sparse user–item interactions. However, existing methods inadequately model the c...
Lithology identification is a critical technology for geological resource exploration and engineering safety assessment. However, traditional methods suffer from insufficient feature representation and low classification accuracy due to challenges su...
Large radiometric and geometric distortion in multi-source images leads to fewer matching points with high matching blunder ratios, and global geometric relationship models between multi-sensor images are inexplicit. Thus, traditional matching blunde...
Predicting the future occupied regions of non-cooperative space objects is critical for close-range situational awareness, on-orbit servicing, and collision risk assessment. Yet, conventional target-wise estimators discard the cross-target uncertaint...
This article proposes a linear-time correlation-controlled shuffling method that attenuates Pearson correlation by permuting one variable through local swaps on a random regular graph. Each round processes a fixed edge order and accepts only vertex-d...
Graphs are an effective data structure for characterizing ubiquitous connections as well as evolving behaviors that emerge in inter-wined systems. Limited by the stereotype of node-to-node connections, learning node representations is often confined...
Full three-dimensional gear-contact analysis resolves localized tensor fields that simplified models cannot recover, but repeated nonlinear finite element (FE) solutions are costly. This study develops a multi-fidelity physics-informed graph surrogat...
Knowledge graph completion (KGC) commonly relies on embedding models trained with observed triples. Multi-hop paths provide additional evidence, but rule-derived triples differ in reliability and should not receive equal training weight. We study how...
Reliable multi-sensor bearing fault diagnosis is challenged by temporal leakage caused by window-level random splitting, limited modeling of cross-sensor dependencies, and inadequate integration of raw temporal dynamics with time-frequency representa...
Modern supply chains operate as highly interconnected networks characterized by decentralization, data silos, and increasing sustainability constraints. Although Graph Neural Networks (GNNs) have demonstrated strong capability in modeling relational...
In applications pertaining to sensor network, the Internet of Things, industrial monitoring, and cyber–physical security, graphs are increasingly being outsourced to clouds, where similarity search should be supported without exposing graph con...
Subdiffusion on graphs is often modeled by time-fractional diffusion equations; yet, its structural and dynamical consequences remain unclear. We show that subdiffusive transport on graphs is a memory-driven process generated by a random time change...
With the increasing number of network security threats and the frequent occurrence of software vulnerability attacks, the effective management and large-scale retrieval of vulnerability data have become urgent needs. Existing vulnerability informatio...
Circular RNAs (circRNAs) are covalently closed single-stranded RNA molecules, which have many biological functions. Previous experiments have shown that circRNAs are involved in numerous biological processes, especially regulatory functions. It has a...
Wireless edge systems increasingly rely on on-device learning for tasks such as user localization and misuser detection, but centralized training is often infeasible because of privacy and bandwidth constraints. In this work, we study communication-e...
In this paper, we present a novel data clustering framework for big sensory data produced by IoT applications. Based on a network representation of the relations among multi-dimensional data, data clustering is mapped to node clustering over the prod...
The innovation capability of seed enterprises reflects their core competitiveness and serves as a vital foundation for sustainable agricultural development and modernization. Therefore, evaluating this capability is of great importance. However, exis...
of 5