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...
We study the structural properties of networks formed by random sets of bit strings—namely the ordered arrays of binary variables representing, for instance, genetic information or cultural profiles. Two bit strings are connected by a network l...
Logistics security, as the lifeline of the economy connecting production, distribution, and consumption, holds a pivotal position in the modern economic system, where any potential threats like natural disasters or cyber attacks could have far-reachi...
There is a widespread belief in the Bayesian network (BN) community that while the overall accuracy of the results of BN inference is not sensitive to the precision of parameters, it is sensitive to the structure. We report on the results of a study...
Rural spatial restructuring in rapidly urbanizing regions is jointly shaped by road expansion and ecological constraints, yet existing studies often examine transportation and ecological systems in isolation. This study develops a coupled “road...
Link prediction based on bipartite networks can not only mine hidden relationships between different types of nodes, but also reveal the inherent law of network evolution. Existing bipartite network link prediction is mainly based on the global struc...
An interconnection network is usually modeled by a graph, and fault tolerance of the interconnection network is often measured by connectivity of the graph. Given a connected subgraph L of a graph G and non-negative integer t, the t-extra connectivit...
Maximum entropy principle (MEP) analysis with few non-zero effective interactions successfully characterizes the distribution of dynamical states of pulse-coupled networks in many fields, e.g., in neuroscience. To better understand the underlying mec...
Industrial network systems are facing various new challenges, such as increasing functional failure factors, the accelerating penetration of information threats, and complex and diverse attack methods. Industrial networks are often vulnerable to natu...
Typically, analyses of meander structures (MSs) for transfer characteristics are conducted using specialized commercial software based on numerical methods. However, these methods can be time-consuming, particularly when a researcher is seeking to pe...
Constructing the structure of protein signaling networks by Bayesian network technology is a key issue in the field of bioinformatics. The primitive structure learning algorithms of the Bayesian network take no account of the causal relationships bet...
Coarse-graining is a powerful tool for extending the reach of dynamic models of proteins and other biological macromolecules. Topological coarse-graining, in which biomolecules or sets thereof are represented via graph structures, is a particularly u...
The assignment of secondary structure elements in protein conformations is necessary to interpret a protein model that has been established by computational methods. The process essentially involves labeling the amino acid residues with H (Helix), E...
This work presents Satellite Style and Structure Generative Adversarial Network (SSGAN), a generative model of high resolution satellite imagery to support image segmentation. Based on spatially adaptive denormalization modules (SPADE) that modulate...
Exploration of the characteristics of innovation adoption in the context of social network will add new insights beyond the traditional innovation models. In this paper, we establish a new agent-based model to simulate the behaviors of agents in ter...
The phenomenon of polarized development among regional cities has sparked extensive contemplation and indicated a need for research on multi-source regional networks. However, such research faces two obstacles: the absence of quantitative measurement...
This paper proposes a structured hierarchical-multilevel approach to calculating the power flows and losses of electricity in radial electrical networks with different nominal voltages at given loads and voltages of the power source. The researched e...
In The cognitive-emotional brain, Pessoa overlooks continuum effects on nonlinear brain network connectivity by eschewing neural field theories and physiologically derived constructs representative of neuronal plasticity. The absence of this content,...
This paper proposes the variable chromosome genetic algorithm (VCGA) for structure learning in neural networks. Currently, the structural parameters of neural networks, i.e., number of neurons, coupling relations, number of layers, etc., have mostly...
Ports play an important role in maintaining the effectiveness of maritime logistics. When ports encounter congestion, strikes, or natural disasters, the maritime container transportation network might be significantly affected. The Beibu Gulf sea are...
The mobility and openness of smart cities characterize them as particularly complex networks, necessitating the resilience enhancement of smart city regions from a network structure perspective. Taking the Chengdu–Chongqing urban agglomeration...
Similarity measures in heterogeneous information networks (HINs) have become increasingly important in recent years. Most measures in such networks are based on the meta path, a relation sequence connecting object types. However, in real-world scenar...
One of the most widespread and important applications in wireless sensor networks (WSNs) is the continuous data collection, such as monitoring the variety of ambient temperature and humidity. Due to the sensor nodes with a limited energy supply, the...
The aim of this paper is to present a novel approach to energy use forecasting. We propose a nested fuzzy cognitive map in which each concept at a higher level can be decomposed into another fuzzy cognitive map, multilayer perceptron artificial neura...
Large-scale wireless sensor networks are characterized by stringent energy and computation restrictions. It is exceedingly difficult to change a sensor network’s environment configurations, such as the number of sensor nodes, after deployment o...
This paper studies the community structure of the bank correlation network in the financial system and analyzes the systemic risk of the community sub-networks. Based on the balance sheet data of U.S. commercial banks from 2008, we establish a bank c...
We present a machine learning approach for developing subgrid-scale (SGS) parameterizations in coarse simulations of partial differential equations. We utilize structure-preserving neural networks and entropy variables to learn subgrid fluxes in coar...
A rich and effective dataset is an important foundation for the development of AI algorithms, and the quantity and quality of the dataset determine the upper limit level of the algorithms. For aerospace remote sensing datasets, due to the high cost o...
Understanding the spatial structure of a megaregion with urban and rural areas is crucial for promoting sustainable urbanization and urban–rural integration. Compared to the city network (or the network of urban areas), however, fewer studies f...
The increase of distributed energy resources (DERs) in low voltage (LV) distribution networks requires the ability to perform an accurate power flow analysis (PFA) in unbalanced systems. The characteristics of a well performing power flow algorithm a...
This study explores the potential of graph neural networks (GNNs) in analyzing brain networks of children and adolescents exposed to trauma, addressing limitations in traditional neuroimaging approaches. MRI-based brain data from trauma-exposed and c...
Clustering ensemble is a research hotspot of data mining that aggregates several base clustering results to generate a single output clustering with improved robustness and stability. However, the validity of the ensemble result is usually affected b...
Hypergraph Neural Networks (HGNNs) have become an important tool for processing complex structured data due to their ability to model higher-order associative relationships. However, the inherent adversarial vulnerabilities of HGNNs may raise serious...
This article focuses on the Assembled Structure (AS) state recognition method based on vibration data. The difficulty of AS state recognition is mainly the extraction of effective classification features and pattern classification. This paper present...
This study presents the development and validation of a complex-valued neural network model to predict wave conditions—such as wave direction, height, and period—surrounding floating structures. Accurate wave predictions are crucial for o...
Brain structure segmentation on magnetic resonance (MR) images is important for various clinical applications. It has been automatically performed by using fully convolutional networks. However, it suffers from the class imbalance problem. To address...
China has entered an era of rapid high-speed railway (HSR) development and the spatial structure of urban agglomerations will evolve in parallel with the development and evolution of the spatial structure of the HSR network. In this study, we explore...
Commercial functions are widely regarded as a key instrument for revitalizing historic cultural districts; however, functional mix decisions are often made on experiential grounds, resulting in heritage erosion, homogenized retail, and weakened distr...
Physics structure-informed neural networks (
Product quality control in chemical processes faces challenges from dynamic non-stationary data, underutilized variable spatial correlations, and overreliance on prior knowledge. This paper addresses these issues by proposing an enhanced Spatio-Tempo...
Internet of Things (IoT) is a promising technology that uses wireless sensor networks to enable data collection, monitoring, and transmission from the physical devices to the Internet. Due to its potential large scale usage, efficient routing and Med...
As mobile networks evolve toward Beyond 5G and 6G architectures, energy efficiency and sustainability have become increasingly critical due to growing traffic volumes, denser base station deployments, and the rising number of connected devices. Suppo...
Polymer electrolytes (PEs) provide enhanced safety for high–energy–density lithium metal batteries (LMBs), yet their practical application is hampered by intrinsically low ionic conductivity and insufficient electrochemical stability, pri...
Protein–protein interfaces (PPIs) are essential for numerous biological processes and represent attractive targets for drug discovery and protein engineering. Although recent protein language models have enabled powerful sequence representation...
The “closed-loop design, open-loop operation” scheme traditionally adopted by distribution networks results in limited power-flow regulation capability and insufficient supply reliability. Existing flexible interconnection solutions mostl...
As global warming accelerates in recent years, the frequency of droughts has increased and water management at the national level has become very important. In particular, accurate understanding and management of the forest is essential as the water...
Vibration-based data-driven structural damage identification methods have gained large popularity because of their independence of high-fidelity models of target systems. However, the effectiveness of existing methods is constrained by critical short...
This paper is devoted to investigating the problem of finite-time (FnT) adaptive cluster synchronization for heterogeneous fractional-order dynamic networks (FODNs) with community structure and co-competition interactions. By designing a suitable ada...
Zooplankton are sensitive indicators of ecological change, but their community reorganization under early-phase restoration remains unclear. This study aims to reveal how their diversity, standing stock, and co-occurrence networks respond to early-ph...
Charging stations and electric vehicle (EV) charging networks signify a significant advancement in technology as a frontier application of the Social Internet of Things (SIoT), presenting both challenges and opportunities for current 6G wireless netw...
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