Dynamic Network Research Articles

ABSTRACT Mega engineering projects often encounter issues such as project delays and cost overruns due to their complexity and uncertainty. To enhance project management effectiveness, it is essential to design a rational engineering transaction governance structure tailored to the characteristics of each engineering project. To address the design issues of transaction governance structures for specific projects, this study first constructs an improved social network model within a dual-layer linguistic term framework to characterize the dynamic evolution of trust relationships among experts and quantify their weights. Subsequently, a hybrid weighting method that integrates the average method, entropy weighting, and game-theoretic approaches is employed to optimize attribute weight calculation, thereby enhancing the objectivity and accuracy of the decision-making process. On this basis, a trust-based reward and penalty mechanism is introduced to facilitate the gradual convergence of group opinions and achieve efficient consensus. Consequently, a multi-attribute group decision-making method is developed for engineering project transaction governance structure optimization, which integrates the dual-layer linguistic term environment with improved dynamic social network modeling and information entropy. Then, the model proposed in this paper is applied to a mega engineering project, verifying the feasibility of the model. Finally, by comparing with other group decision-making methods, the effectiveness and accuracy of the decision model for the governance structure decision-making in mega engineering project transactions are validated. The results could provide support for the decision-making of the governance structure in project legal entity transactions.

Novel insights into sources identification of fluvial dissolved organic matter in intricate plain river networks using optical indices with grey influence analysis and Bayesian model.

With the rapid global transition towards clean energy, wind-powered heating systems have emerged as a critical solution for efficient wind energy utilization, particularly in the northern regions of China. However, these systems face significant reliability challenges due to complex spatiotemporal couplings and harsh operating conditions. This paper presents an adaptive fault prediction and intelligent diagnosis method based on a Multi-level Spatiotemporal Graph Neural Network to address the challenges of multi-source data fusion difficulties and inadequate spatiotemporal feature extraction. The proposed framework establishes a dynamic adaptive threshold generation mechanism by integrating maximum a posteriori probability estimation with interquartile range analysis, enabling real-time system state monitoring and early fault warning. The methodology incorporates graph attention networks, seven-branch parallel subgraph architectures, and multi-head attention mechanisms to capture topological evolution patterns through dynamic graph neural networks, while temporal attention modules are employed to enhance sequential dependencies of critical parameters. Experimental validation was conducted using 42TB of SCADA data from China Guoneng Group's 200MW wind-heat cogeneration project. The results demonstrate the model's superior multi-level diagnostic capability, achieving a comprehensive prediction accuracy of 93.5% and a fault detection Fβ-score (β = 0.5) of 0.95-representing an 18.6% improvement over traditional approaches-while maintaining strong robustness (KL divergence 0.09 ± 0.02) under transient operating conditions.

BackgroundSystemic lupus erythematosus (SLE) is a complex autoimmune disease with a scarcity of effective treatment options and considerable side effects linked to current therapies. Withania somnifera, is rich in phytochemicals that have demonstrated immunomodulatory and anti-inflammatory effects, suggesting its promise as a natural therapeutic candidate for SLE.MethodsAn in silico methodology explored the therapeutic potential of W. somnifera phytocompounds for SLE. Phytochemicals were obtained from Indian Medicinal Plants, Phytochemistry, and Therapeutics (IMPPAT) and KNApSAcK databases, followed by virtual screening using SwissADME, MOLSOFT, and ProTox 3.0 to identify drug-like and non-toxic candidates. Target genes were predicted using SwissTargetPrediction and STITCH, while SLE-associated genes were compiled from GeneCards and Online Mendelian Inheritance in Man (OMIM). The intersection of these genes was analyzed to construct a protein-protein interaction network, with hub genes identified through Cytoscape. Molecular docking and 100 ns Molecular Dynamic simulations, with Molecular Mechanics, General Born Surface Area (MM-GBSA) free energy calculations, were conducted for lead compounds against top hub proteins.ResultsThe study identified three phytocompounds-vanillic acid, (+)-catechin, and withanolide K-that show favorable pharmacokinetic and toxicity characteristics. Network analysis identified 161 common target genes, with Caspase 3 (CASP3), HIF1A (Hypoxia-inducible factor 1-alpha subunit), Interleukin 1 beta (IL1B), and Interleukin 6 (IL6) as significant hub proteins. Docking studies revealed (+)-catechin and withanolide K have strong binding affinities with IL6 and CASP3. Molecular dynamics simulations confirmed complex stability, and MM-GBSA calculations showed favorable binding free energies, especially in (+)-catechin-protein interactions.Conclusions(+)-Catechin and withanolide K are promising biomolecules for SLE, demonstrating a strong binding affinity with key proteins linked to the disease. These results offer a computational basis for experimental validation and the potential development of safer, plant-based therapies for SLE.

The increasing accessibility of high-throughput omics technologies has represented a paradigm change in systems biology, facilitating the systematic exploration of biological complexity at genomic, transcriptomic, proteomic, and metabolomic levels. Contemporary systems biology more and more depends on integrative multi-omics strategies to unravel the sophisticated, dynamic networks of cellular function and organismal phenotypes. Such methodologies enable scientists to clarify molecular interactions, decipher disease pathology, identify strong biomarkers, and guide precision medicine and synthetic biology initiatives. Recent technological breakthroughs in computational tools, ranging from early or late data integration, network analysis, and machine learning, have overcome obstacles of high-dimensionality, heterogeneity, and perturbations restricted to specific contexts. In this review, we critically assess the principles, methods, and applications of multi-omics integration, with an emphasis on cancer biology, microbial engineering, and synthetic biology. We showcase case studies in which integrative omics provided actionable findings. Finally, we address current limitations (e.g., data heterogeneity, interpretability) and forthcoming solutions (artificial intelligence, single-cell omics, cloud platforms). By closing the gap between molecular layers, multi-omics integration is moving toward predictive models of biological systems and revolutionary biotechnological applications.

Objective. Several data-driven approaches based on information theory have been proposed for analyzing high-order interactions (HOIs) involving three or more components of a network system. The existing methods do not account for temporal correlations in the data, or are defined only in the time domain and rely on the assumption of stationarity in the underlying dynamics, making them inherently unable to detect frequency-specific behaviors and track transient functional links in physiological networks.Approach. This study introduces a new framework which enables the time-varying and time-frequency analysis of HOIs in networks of random processes through the spectral representation of vector autoregressive models. The time- and frequency-resolved analysis of synergistic and redundant interactions among groups of processes is ensured by a robust identification procedure based on a recursive least squares estimator with a forgetting factor.Main results. Validation on simulated networks illustrates how the time-frequency analysis is able to highlight transient synergistic behaviors emerging in specific frequency bands which cannot be detected by time-domain stationary analyzes. The application on brain evoked potentials in rats elicits the presence of redundant information timed with whisker stimulation and mostly occurring in the contralateral hemisphere. The application to cardiovascular oscillations reveals a reduction in redundant information following head-up tilt, reflecting a functional disconnection within the physiological network of heart period, respiratory, and arterial pressure signals.Significance. The proposed framework enables a comprehensive time-varying and time-frequency analysis of the hierarchical organization of dynamic networks. As our approach goes beyond pairwise interactions, it is well suited for the study of transient high-order behaviors arising during state transitions in many network systems commonly studied in physiology, neuroscience and other fields.

Freezing of gait (FOG) in Parkinson's disease (PD) is a disabling motor symptom with unclear pathophysiology. Beyond known basal ganglia dysfunction and cognitive impairment, sensory integration deficits are increasingly key, though their cortical network mechanisms during walking remain poorly understood. This study used functional near-infrared spectroscopy (fNIRS) during sensory-contextual walking to clarify FOG-related cortical networkdynamics, aiming to: (1) Characterize FOG-specific cortical responses to sensory challenges; (2) Distinguish PD-specific versus FOG-associated corticalnetwork dysfunction; (3) Explore dopaminergic modulation of these mechanisms. 40 PD patients [20 with FOG (PD-FOG), 20 without FOG (PD-nFOG)] and 23 healthy-controls (HC) completed four walking tasks [routine Walking-on-Ground (WG), Walking-on-Foam with proprioceptive challenge (WF), Walking-through-Narrow-gate with visuospatial challenge (WN), and Walking-through-Slope with combined proprioceptive/visuospatial challenges (WS)] under OFF and ON dopaminergic medications. Cortical hemodynamics in the Supplementary Motor Area (SMA), Primary Motor Cortex (M1), Primary Somatosensory Cortex (S1), Somatosensory Association Cortex (SAC), Prefrontal Cortex (PFC), and Frontal Eye Field (FEF), as well as walking performance were recorded. Group and medication-state differences were analyzed using ANOVA models. (1) PD-FOG lacked adaptive cortical plasticity (activation/connectivity response) to increasing sensory demands compared to PD-nFOG/HC; (2) PD-FOG exhibited cognitive-sensory/motor hyperconnectivity versus PD-nFOG during WF. PD patients showed widespread sensory-mediated hyperconnectivity and focal sensorimotor hyperactivation during WG/WN versus HC, but FEF hypoactivation during WS; (3) Medication improved gait but suppressed sensorimotor activation and reduced frontoparietal connectivity in PD-FOG; (4) Elevated M1-SMA connectivity predicted OFF-medication FOG, while elevated S1-FEF connectivity predicted ON-medication FOG. Daily FOG severity correlated with distinct OFF-/ON-medication connectivity patterns. This study reveals a medication-dependent, biphasic cortical dysfunction in PD-FOG. The OFF-medication state shows compensatory hyperconnectivity that fails under sensory challenges, indicating deficient plasticity, whereas the ON-medication state exhibits a paradoxical suppression of sensorimotor and integrative networks despite gait improvement. This reconceptualizes FOG as a dynamic network adaptation failure and points to state-specific therapeutic strategies. Registration number ChiCTR2300072744.

Social networks are shaped by complex, intersecting identities that drive our connection preferences. These preferences create tie inequalities: Systematic differences in the number of links members of different groups accumulate. Understanding tie inequalities is critical because they contribute to disparities in social capital, with downstream consequences for access to opportunities and resources. Previous research has examined the impact of single-dimensional identities on tie disparities, but when multiple identities intersect, network disadvantages accumulate nonlinearly, disproportionately harming individuals belonging to several disadvantaged groups. However, how multidimensional connection preferences affect network dynamics and amplify or mitigate differences in ties remains unknown. Using a network model, we characterize the effects of multidimensional preferences and attribute correlation. We find that correlation creates counterintuitive tie inequalities unobservable in one-dimensional systems. We calibrate the model with high school friendship data and derive closed-form expressions for tie inequalities, which reproduce the empirical patterns with remarkable accuracy. Our findings have substantial implications for addressing intersectional inequalities in social networks.

This paper presents an integrated approach to social network analysis that combines graph theory, social network analysis (SNA), and Shannon’s information theory, applied to a real-world Twitter network built around the political hashtag Zandberg. Unlike studies based on synthetic data, our analysis leverages empirical content from a live political discourse. We employ classical centrality measures (degree, betweenness, closeness), local clustering coefficients, and community detection using the Louvain algorithm. A key theoretical contribution is the introduction of a novel metric: the Structural Entropy Index of a Community (SEIC), which quantifies internal decentralization of communication independently of community size. The analysis reveals significant variation in community structures and entropy levels. Larger communities tend to be decentralized (SEIC > 0.8), while smaller groups are often dominated by single influential nodes. These findings have practical implications for influencer identification, disinformation resilience assessment, and communication strategy optimization. The proposed methodological framework provides a robust tool for studying the structural and informational dynamics of real-world social networks in digital environments.

SUMMARYBacteria are frequently subject to potentially lethal temperature shifts in their natural environments. We review the changes in the structure and dynamics of the gene regulatory network of the bacterium Escherichia coli during cold shocks. First, we describe the effects of cold shocks on higher-order cellular structures (cytoplasm and membrane) and functions (growth, division, and biofilm formation). Next, we focus on the nucleoid, DNA supercoiling, topoisomerases, ATP, and nucleoid-associated proteins. Afterward, we describe the mutual effects of changes in transcription dynamics and DNA supercoiling during cold shocks, followed by the consequent genome-wide, time-lapse changes in the transcriptome. Finally, we briefly describe the post-transcriptional effects of cold shocks and the cellular processes of acclimatization. In the end, we discuss how studying this topic can assist in developing temperature-sensitive synthetic genetic circuits, efficient bioindustrial processes, and new means to cope with bacterial antibiotic tolerance.

Purpose The prominence of online social networks (OSNs) has made them ideal platforms for viral marketing. Otome games efficiently use OSNs for viral campaigns, making them a valuable case for studying viral marketing strategies. This study identified key elements of viral marketing posts to inform the operational strategies of commercial accounts. Design/methodology/approach This study analyzed China's sizable otome game market using operational behavioral data from the top five games, compiled into the OtomeVM dataset. Following the knowledge discovery in databases framework, this study identified key characteristics of the top 25% most viral posts and proposed the ViralGD model, a multimodal machine learning model for virality prediction. The model's decision logic was further interpreted through a global surrogate method to ensure transparency. Findings This study identified lottery mechanisms as significant predictors of post virality, with optimal performance observed for content combining short videos of 180 s or less and long-text descriptions of 175 characters or more. Commercial social media accounts often produced emotionally homogeneous content, with emotions having minimal impact on the viral spread of their posts. Originality/value This study is the first to apply large-scale real-world data and data mining to uncover overlooked patterns in viral marketing, offering theoretical insights into defining virality and refining campaign design. Practically, the iterative application of the ViralGD model uncovers high-impact features that boost content diffusion, effectively guiding operators in selecting optimal improvements.

Cost and revenue efficiency analysis in dynamic network DEA with target setting: a value-based approach

Malware detection and classification in network traffic is a critical challenge in cybersecurity, with evolving threats that traditional methods struggle to address. As network traffic becomes more complex, accurately identifying malicious activities while minimizing false positives is essential for real-time monitoring systems. This study aims to enhance malware detection using deep learning (DL) techniques, focusing on improving accuracy, reducing false positives, and enabling real-time detection in dynamic network environments. Several advanced DL techniques are introduced to address these challenges. Entropy-Based Traffic Filtering (ETF) measures the randomness in network traffic to identify anomalies and malicious patterns, reducing noise and improving feature extraction. Self-Supervised Learning for Anomaly Detection (SSLAD) detects malware without labeled data by learning normal traffic patterns and identifying anomalies, thus improving the detection of unknown threats. Graph Neural Networks for Malware Traffic Classification (GNN-MTC) model network traffic as graphs, where devices are nodes, and communications are edges, capturing relational dependencies and anomalies to detect complex attack patterns like botnets and command-and-control (C2) communications. Context-Aware Graph Attention Networks (CA-GAT) further enhance detection by analyzing traffic as graphs while incorporating contextual factors like time and behavior, focusing on relevant interactions to improve attack detection. The proposed DL model achieves 98% accuracy, surpassing DeepMAL (95%) and an entropy-based method by Huang etal. (97.3%). Its strong precision and recall demonstrate superior performance in detecting known and novel malware, making it well-suited for real-time network security applications. The model was implemented using Python. Future research could focus on integrating real-time adaptive learning models, exploring hybrid DL architectures, and enhancing cross-platform malware detection, ensuring scalability and robustness in evolving network security environments.

Exosome-targeted injectable hydrogels for sustained DEPTOR delivery and delay of IDD via the mTORC1/SASP pathway.

Protein-based bioadhesives are emerging as sustainable alternatives to traditional adhesives, with potential applications in biomedicine, tissue engineering, and electronics. However, challenges such as low adhesive strength, poor substrate and environmental adaptability, and limited recyclability persist. Unlike previous studies that focused on replicating key chemical units, interaction patterns, liquid-liquid phase separation (LLPS), and nano/microscale structures in natural adhesives, a novel strategy is proposed that partially disrupts the noncovalent interactions between polyphenols and proteins through amino acid modulation. This approach facilitates the migration and enrichment of polyphenol adhesive molecules at substrate interfaces along with amino acid, where they synergistically enhance substrate adaptability. The high cohesion and dynamic nature of the resulting network, driven by noncovalent interactions, ensures both high adhesive strength and full recyclability of polyphenol-protein adhesives (PPA). Importantly, this strategy expands the PPA toolbox, incorporating proteins (gelatin, collagen, silk, keratin) and polyphenols (tannic acid, proanthocyanidins, epigallocatechin gallate), broadening their applications in daily-use adhesives, industrial products, and cultural relic restoration across both dry and wet environments. This work enhances the understanding of balancing adhesion and cohesion, providing insights for the design of high-performance bioadhesives.

An inherent challenge in designing laboratory-grown, engineered living neuronal networks lies in predicting the dynamic repertoire of the resulting network and its sensitivity to experimental variables. To fill this gap, and inspired by recent experimental studies, we present a numerical model designed to replicate the anisotropies in connectivity introduced through engineering, characterize the emergent collective behavior of the neuronal network, and make predictions. The numerical model is developed to replicate experimental data, and subsequently used to quantify network dynamics in relation to tunable structural and dynamical parameters. These include the strength of imprinted anisotropies, synaptic noise, and average axon lengths. We show that the model successfully captures the behavior of engineered neuronal cultures, revealing a rich repertoire of activity patterns that are highly sensitive to connectivity architecture and noise levels. Specifically, the imprinted anisotropies promote modularity and high clustering coefficients, substantially reducing the pathological-like bursting of standard neuronal cultures, whereas noise and axonal length influence the variability in dynamical states and activity propagation velocities. Moreover, connectivity anisotropies significantly enhance the ability to reconstruct structural connectivity from activity data, an aspect that is important to understand the structure-function relationship in neuronal networks. Our work provides a robust in silico framework to assist experimentalists in the design of in vitro neuronal systems and in anticipating their outcomes. This predictive capability is particularly valuable in developing reliable brain-on-a-chip platforms and in exploring fundamental aspects of neural computation, including input-output relationships and information coding.

Soil microbiomes, critical for plant productivity and ecosystem functioning, mediate essential functions such as pathogenesis, mutualism, and decomposition through different fungal functional groups. Yet, our understanding of the dynamics of co-existing soil fungal functional groups in the rhizosphere remains limited. By leveraging urban farming-featuring fields of different ages and multiple genotypes-we tracked the relative abundance, richness, and microbial networks of putative plant pathogenic fungi, mycorrhizal fungi, and saprotrophic fungi across fields over two years. We observed an increase in the relative abundance of putative plant pathogenic fungi in the rhizosphere in older fields relative to younger fields, supporting the prediction of pathogen accumulation over time. In contrast, there was a decrease in the relative abundance of mycorrhizal fungi in older fields. The relative abundance of saprotrophic fungi remained similar between younger and older fields. While the richness of putative plant pathogenic fungi and saprotrophic fungi was similar across the examined fields, the community structure of both groups differed between younger and older fields. For mycorrhizal fungi, the richness declined in older fields and over the two years. These dynamics led to distinct microbial networks, with decreased network links for mycorrhizal fungi and increased links for saprotrophic fungi in older fields, whereas the links for plant pathogenic fungi remained similar across fields. Our study reveals contrasting dynamics of essential soil fungal functional groups in the rhizosphere and provides predictive insight into the potential shifts in soil function and their impact on plant productivity.

In the digital era, algorithms and data systems have profoundly reshaped social relations in China, transforming traditional structures into fluid, dynamic networks. Drawing on Fei Xiaotong's "differential mode of association," which views Chinese society as ego-centered with graded ties, and Zygmunt Bauman's "liquid modernity," emphasizing transient and unbound interactions, this study introduces the concept of a "reversible liquid differential order." This framework captures how social ties form, dissolve, and reconfigure rapidly in digital contexts, particularly on platforms like Xiaohongshu, a social e-commerce app targeting young women. The research explores modular and elastic social relations on Xiaohongshu through a qualitative case study. Data were collected from 20 public notes and approximately 50 related comments under the #LifeSharing tag in 2025, selected for high interaction levels and relevance to relational dynamics. Thematic analysis was employed to code patterns, identifying themes such as temporary connections, algorithm-driven intimacy gradients, and guanxi culture. Findings revealed that Xiaohongshu's algorithms curate personalized content, fostering ephemeral ties through notes, comments, and recommendations, while users adapt with resilience. The study concluded that this reversible order integrates cultural specificity with algorithmic elasticity, enhancing user well-being but also amplifying social anxiety. Theoretically, it advances network analysis for quantifying cultural patterns; practically, it informs platform designs to mitigate relational uncertainties in digital China.

The 2022 outbreak of mpox affected more than 80,000 individuals worldwide, most of whom were gay, bisexual, and other men who have sex with men (GBMSM) who likely contracted the disease through close contact during sex. Given the unprecedented number of mpox infections and the new route of infection, there was substantial uncertainty about how best to manage the outbreak. We implemented a dynamic agent-based network model to simulate the spread of mpox in a United States-based GBMSM population. This model allowed us to implement data-informed dynamic network evolution to simulate realistic disease spreading and behavioral adaptations. We found that behavior change, the reduction in one-time partnerships, and widespread vaccination are effective in preventing the transmission of mpox, and that earlier vaccination and behavior adaptation has a greater effect, even when only a high-activity portion of the population participates. With no vaccination or behavior adaptation, 16% of the population was infected (25th percentile, 75th percentiles of simulations: 15.3%, 16.6%). With vaccination and behavior change in only the 25% of GBMSM most likely to have a one-time partner, cumulative infections were reduced by 30%, or a total reduction in nearly 500 infections (mean: 11.3%, [Formula: see text] and [Formula: see text]: 9.4%, 13.4%). Earlier vaccination and behavior adaptation further reduce cumulative infections; beginning vaccination a year before the outbreak results in only 2.6% of GBMSM being infected, averting 1300 infections or nearly 13% of the total population in our model. We also show that sustained partnerships drive the early outbreak, while one-time partnerships drive transmission after the first initial weeks. The median effective reproductive number, [Formula: see text], at [Formula: see text] days is 1.40 for casual partnerships, 1.00 for main, and 0.35 for one-time. By [Formula: see text], the median [Formula: see text] for one-time partnerships more than tripled to 1.47, while it decreases for casual and main partnerships: 0.37 and 0.19, respectively. With the ability to model individuals' behavior, mechanistic networks are particularly well suited to studying sexually transmitted infections, the spread and control of which are often governed by individual-level action. Our results contribute valuable insights into the role of different outbreak mitigation strategies and relationship types in mpox transmission dynamics.

We propose a dynamic latent space network model that incorporates the popularity effect, offering a unified framework to simultaneously account for the popularity and proximity effects in network dynamics. The model represents node attributes as latent positions, enabling the analysis of connectivity patterns influenced by both proximity and popularity while quantifying their relative contributions. A Bayesian inference algorithm is developed to estimate model parameters and latent positions, and its effectiveness is validated through simulation studies. The proposed model is applied to Bitcoin trust networks (OTC and Alpha), revealing key insights into their structural evolution and the distinct roles of popularity and proximity effects. Our findings demonstrate the versatility of the model in capturing temporal dynamics and supporting applications such as node recommendation and network visualization.