Large-scale Networks Research Articles

Underwater Network Time Synchronization Method Based on Probabilistic Graphical Models

In underwater clustering and benchmark networks, nodes need to reduce the rate and energy consumption of acoustic communication while ensuring synchronization accuracy. In large-scale networks, the improvement in the efficiency of existing network time synchronization often relies on the optimization of topological structures, and the improvement in efficiency within local areas is limited. This paper proposes a method to synchronize underwater time using the probability graph model. The method utilizes the positional and motion status information of sensor networks to construct a factor graph model for distributed network synchronization. By simplifying the marginal probability density function of the system clock difference, it can quickly calculate the clock difference parameters of nodes, thereby effectively improve the synchronization efficiency. The experimental results show that the method can complete global time synchronization within a cycle while achieving a clock difference correction accuracy higher than seconds, which significantly optimized the synchronization cycle and efficiency, and reduced the energy consumption of the acoustic communication.

New LMI-based criteria for robust finite-time stability of singular large-scale neural networks with interacted delays

New LMI-based criteria for robust finite-time stability of singular large-scale neural networks with interacted delays

Two Accelerated Non-backtracking PageRank Algorithms for Large-scale Networks

Two Accelerated Non-backtracking PageRank Algorithms for Large-scale Networks

An innovative framework to securely transfer data through the Internet of Things using advanced generative adversarial networks

The Internet of Things (IoT) is developing so quickly that cloud-centric computing finds it challenging to keep up with the demands of usability and low latency. Edge computing unifies computing, networking, storage and applications into a distributed open system. At the edge of the IoT, it provides intelligent services. The edge network is made up of several wired and wireless networks, and edge nodes have constrained amounts of memory and processing power. The edge network is vulnerable to several types of cyberattacks because of these factors. Large-scale network data gathering and detection for IoT security is also challenging for an IoT edge node to provide. Data analytics for intrusion detection guarantees high accuracy of intrusion detection systems (IDSs), but the implementation of such algorithms on IoT might be a challenge due to the limited resources on edge nodes. Inspired in part by these challenges, we suggest a sophisticated IDS based on a generative adversarial network (GAN). This article suggests a novel method for detecting intrusions in the IoTs networks that make use of Colony Predator Algorithm (CPA) for optimization of the detection process. Through the use of GANs to create real-looking data samples, the suggested technique helps to have reliable anomaly and intrusion detection. The integration improves the training process, making it more efficient and enabling the CPA system to better differentiate between benign and malicious activities, which exponentially raises the system's efficiency.

A Hybrid Probabilistic Graph Based Community Clustering Model for Large Social Networking Link Prediction Data

Dynamic community clustering is essential for online social networking sites due to the high dimensionality and large data size. It aims to uncover social relationships among nodes and links within the network. However, traditional models often struggle with community structure detection because of the extensive computational time and memory required. Additionally, these models need contextual weighted node information to establish social networking feature relationships. To address these challenges, an advanced probabilistic weighted community detection framework has been developed for large-scale social network data. This framework uses a filter-based probabilistic model to eliminate sparse values and identify weighted community detection nodes for dynamic clustering analysis. Experimental results demonstrate that this filter-based probabilistic community detection framework outperforms others in terms of normalized mutual information, entropy, density, and runtime efficiency (measured in milliseconds).

Efficient Zero-Knowledge Proofs for Set Membership in Blockchain-Based Sensor Networks: A Novel OR-Aggregation Approach

Blockchain-based sensor networks offer promising solutions for secure and transparent data management in IoT ecosystems. However, efficient set membership proofs remain a critical challenge, particularly in resource-constrained environments. This paper introduces a novel OR-aggregation approach (where “OR” refers to proving that an element equals at least one member of a set without revealing which one) for zero-knowledge set membership proofs, tailored specifically for blockchain-based sensor networks. We provide a comprehensive theoretical foundation, detailed protocol specification, and rigorous security analysis. Our implementation incorporates optimization techniques for resource-constrained devices and strategies for integration with prominent blockchain platforms. Extensive experimental evaluation demonstrates the superiority of our approach over existing methods, particularly for large-scale deployments. Results show significant improvements in proof size, generation time, and verification efficiency. The proposed OR-aggregation technique offers a scalable and privacy-preserving solution for set membership verification in blockchain-based IoT applications, addressing key limitations of current approaches. Our work contributes to the advancement of efficient and secure data management in large-scale sensor networks, paving the way for wider adoption of blockchain technology in IoT ecosystems.

Fast Identification of Critical Nodes in Complex Network Based on Improved Greedy Algorithm

Abstract Over the past decades, many critical and complex systems, such as power grid, transportation network, and information network, have been effectively modeled using complex network. However, these networks are susceptible to cascading failure, triggered by minor failure, leading to partial or total collapse. Preventing cascading failure necessitates the protection of critical nodes within the network, making the identification of these nodes particularly crucial. In this paper, we introduce an Improved Greedy Algorithm (IGA), inspired by the traditional greedy algorithm and the relationship between the propagation mechanism of cascading failure and N-K failure. This algorithm gets rid of the shortcomings of traditional recognition algorithms for dealing with large-scale networks with long time and low accuracy, and evaluates the critical degree of nodes based on network connectivity and overload rate. The simulation is carried out in Barabsi-Albert (BA) network and IEEE 39-, 118-bus systems, and make comparisons with other different algorithms. The results show that IGA not only has low computational complexity, but also has high accuracy in identifying critical nodes in complex networks.

Asynchronous quantum repeater using multiple quantum memory.

A full-fledged quantum network relies on the formation of entangled links between remote location with the help of quantum repeaters. The famous Duan-Lukin-Cirac-Zoller quantum repeater protocol is based on long distance single-photon interference, which not only requires high phase stability but also cannot generate maximally entangled state. Here, we propose a quantum repeater protocol using the idea of post-matching, which retains the same efficiency as the single-photon interference protocol, reduces the phase-stability requirement and can generate maximally entangled state in principle. We also outline an implementation of our scheme based on the Kerr nonlinear resonator. Numerical simulations show that our protocol has its superiority by comparing with existing protocols under a generic noise model and show the feasibility of building a large-scale quantum communication network with our scheme. We believe our work represents a crucial step towards the construction of a fully-connected quantum network.

Bessel beam optical coherence microscopy enables multiscale assessment of cerebrovascular network morphology and function

Understanding the morphology and function of large-scale cerebrovascular networks is crucial for studying brain health and disease. However, reconciling the demands for imaging on a broad scale with the precision of high-resolution volumetric microscopy has been a persistent challenge. In this study, we introduce Bessel beam optical coherence microscopy with an extended focus to capture the full cortical vascular hierarchy in mice over 1000 × 1000 × 360 μm3 field-of-view at capillary level resolution. The post-processing pipeline leverages a supervised deep learning approach for precise 3D segmentation of high-resolution angiograms, hence permitting reliable examination of microvascular structures at multiple spatial scales. Coupled with high-sensitivity Doppler optical coherence tomography, our method enables the computation of both axial and transverse blood velocity components as well as vessel-specific blood flow direction, facilitating a detailed assessment of morpho-functional characteristics across all vessel dimensions. Through graph-based analysis, we deliver insights into vascular connectivity, all the way from individual capillaries to broader network interactions, a task traditionally challenging for in vivo studies. The new imaging and analysis framework extends the frontiers of research into cerebrovascular function and neurovascular pathologies.

Brain neurovascular coupling in amyotrophic lateral sclerosis: Correlations with disease progression and cognitive impairment.

'Neurovascular coupling' (NVC) alterations, assessing the interplay between local cerebral perfusion and neural activity within a given brain region or network, may reflect neurovascular unit impairment in amyotrophic lateral sclerosis (ALS). The aim was to explore NVC as a correlation between the functional connectivity and cerebral blood flow within the large-scale resting-state functional magnetic resonance imaging brain networks in a sample of ALS patients compared to healthy controls (HCs). Forty-eight ALS patients (30 males; mean age 60.64 ± 9.62 years) and 32 HC subjects (14 males; mean age 55.06 ± 16 years) were enrolled and underwent 3 T magnetic resonance imaging. ALS patients were screened by clinical and neuropsychological scales and were retrospectively classified as very fast progressors (VFPs), fast progressors and slow progressors (SPs). Neurovascular coupling reduction within the default mode network (DMN) (p = 0.005) was revealed in ALS patients compared to HCs, observing, for this network, significant NVC differences between VFP and SP groups. Receiver operating characteristic curve analysis showed that impaired NVC in the DMN at baseline best discriminated VFPs and SPs (area under the curve 75%). Significant correlations were found between NVC and the executive (r = 0.40, p = 0.01), memory (r = 0.32, p = 0.04), visuospatial ability (r = 0.40, p = 0.01) and non-ALS-specific (r = 0.40, p = 0.01) subscores of the Edinburgh Cognitive and Behavioural ALS Screen. The reduction of brain NVC in the DMN may reflect largely distributed abnormalities of the neurovascular unit. NVC alterations in the DMN could play a role in anticipating a faster clinical progression in ALS patients, aiding patient selection and monitoring during clinical trials.

A novel tucker decomposition driven taxi travel demand forecasting algorithm

<p>This research aims to enhance taxi travel demand forecasting for sustainable urban traffic management and planning. We extend the Seasonal Autoregressive Integrated Moving Average model into a high-dimensional tensor form by treating the urban transport network as a Euclidean space and introducing Tucker decomposition. This novel approach, both theoretically significant and practically applicable, represents time series data as tensors to better capture multimodal structures and correlations, improving predictive accuracy. Tucker decomposition reduces computational complexity and memory requirements, making it ideal for large-scale urban traffic network prediction. Experiments on six real-world datasets show that the model’s MAE and RMSE are reduced by about 39.43% and 27.01% on average, respectively, compared to the baseline model. Notably, the model is computationally very efficient and takes only a relatively short time to train., suitable for real-time traffic management, congestion mitigation, and resource optimization. In summary, this work innovates time series analysis, providing an efficient and precise tool for urban traffic management and planning, contributing to sustainable urban transportation advancement.<strong></strong></p>

Brain connectomes in youth at risk for serious mental illness: a longitudinal perspective.

Identifying biomarkers for serious mental illnesses (SMI) has significant implications for prevention and early intervention. In the current study, changes in whole brain structural and functional connectomes were investigated in youth at transdiagnostic risk over a one-year period. Based on clinical assessments, participants were assigned to one of 5 groups: healthy controls (HC; n = 33), familial risk for serious mental illness (stage 0; n = 31), mild symptoms (stage 1a; n = 37), attenuated syndromes (stage 1b; n = 61), or discrete disorder (transition; n = 9). Constrained spherical deconvolution was used to generate whole brain tractography maps, which were then used to calculate connectivity matrices for graph theory analysis. Graph theory was also used to analyze correlations of functional magnetic resonance imaging (fMRI) signal between pairs of brain regions. Linear mixed models revealed structural and functional abnormalities in global metrics of small world lambda, and resting state networks involving the fronto-parietal, default mode, and deep grey matter networks, along with the visual and dorsal attention networks. Machine learning analysis additionally identified changes in nodal metrics of betweenness centrality in the angular gyrus and bilateral temporal gyri as potential features which can discriminate between the groups. Our findings further support the view that abnormalities in large scale networks (particularly those involving fronto-parietal, temporal, default mode,and deep grey matter networks) may underlie transdiagnostic risk for SMIs.

Multi-Protocol Relay Chaining for Large-Scale Quantum Key Distribution Networks

Abstract As the first stage of the quantum Internet, quantum key distribution (QKD) networks hold the promise of providing long-term security for diverse users. Most existing QKD networks have been constructed based on independent QKD protocols, and they commonly rely on the deployment of single-protocol trusted relay chains for long reach. Driven by the evolution of QKD protocols, large-scale QKD networking is expected to migrate from a single-protocol to a multi-protocol paradigm, during which some useful evolutionary elements for the later stages of the quantum Internet may be incorporated. In this work, we delve into a pivotal technique for large-scale QKD networking, namely multi-protocol relay chaining. A multi-protocol relay chain is established by connecting a set of trusted/untrusted relays relying on multiple QKD protocols between a pair of QKD nodes. The structures of diverse multi-protocol relay chains are described, based on which the associated model is formulated and the policies are defined for the deployment of multi-protocol relay chains. Furthermore, we propose three multi-protocol relay chaining heuristics. Numerical simulations indicate that the designed heuristics can effectively reduce the number of trusted relays deployed and enhance the average security level versus the commonly used single-protocol trusted relay chaining methods on backbone network topologies.

Injection locking in DC-driven spintronic vortex oscillators via surface acoustic wave modulation

Control of the microwave signal generated by spin-transfer torque oscillators (STOs) is crucial for their applications in spin wave generation and neuromorphic computing. This study investigates injection locking of a DC-driven vortex STO using surface acoustic waves (SAWs) to enhance the STO’s signal and allow for its synchronization with external inputs. We employ a simplified model based on Thiele’s formalism and highlight the role of vortex deformations in achieving injection locking. Micromagnetic simulations are conducted to validate our theoretical predictions, revealing how the locking bandwidth depends on SAW amplitude, as well as on the amplitude and direction of an applied external field. Our findings are pivotal for advancing experimental research and developing efficient low-power synchronization methods for large-scale STO networks.

Change in striatal functional connectivity networks across 2 years due to stimulant exposure in childhood ADHD: results from the ABCD sample.

Widely prescribed for Attention-Deficit/Hyperactivity Disorder (ADHD), stimulants (e.g., methylphenidate) have been studied for their chronic effects on the brain in prospective designs controlling dosage and adherence. While controlled approaches are essential, they do not approximate real-world stimulant exposure contexts where medication interruptions, dosage non-compliance, and polypharmacy are common. Brain changes in real-world conditions are largely unexplored. To fill this gap, we capitalized on the observational design of the Adolescent Brain Cognitive Development (ABCD) study to examine effects of stimulants on large-scale bilateral cortical networks' resting-state functional connectivity (rs-FC) with 6 striatal regions (left and right caudate, putamen, and nucleus accumbens) across two years in children with ADHD. Bayesian hierarchical regressions revealed associations between stimulant exposure and change in rs-FC of multiple striatal-cortical networks, affiliated with executive and visuo-motor control, which were not driven by general psychotropic medication. Of these connections, three were selective to stimulants versus stimulant naive: reduced rs-FC between caudate and frontoparietal network, and between putamen and frontoparietal and visual networks. Comparison with typically developing children in the ABCD sample revealed stronger rs-FC reduction in stimulant-exposed children for putamen and frontoparietal and visual networks, suggesting a normalizing effect of stimulants. 14% of stimulant-exposed children demonstrated reliable reduction in ADHD symptoms, and were distinguished by stronger rs-FC reduction between right putamen and visual network. Thus, stimulant exposure for a two-year period under real-world conditions modulated striatal-cortical functional networks broadly, had a normalizing effect on a subset of networks, and was associated with potential therapeutic effects involving visual attentional control.

Changes in resting-state functional connectivity of large-scale brain networks in bulimia nervosa: evidence from causal analysis.

Bulimia nervosa (BN) has been observationally linked to the functional connectivity (FC) of large-scale brain networks, but the biological mechanisms remain unclear. This study used two-sample Mendelian randomization (MR) with genetic variations as instrumental variables (IVs) to explore potential causal relationships between FC and BN. Summary data from genome-wide association studies (GWAS) involving 2,564 individuals were analyzed to identify genetically predicted BN. Functional magnetic resonance imaging parameters and materials were sourced from the UK Biobank. The variables underwent independent component analysis processing by the database to generate the final GWAS dataset. Various methods, including MR Pleiotropy RESidual Sum and Outlier, MR Egger, and weighted median, were employed to detect heterogeneity and pleiotropy, with inverse variance weighting serving as the principal estimation method (P < 0.05). The FC imaging-derived phenotypes revealed that BN exerted a causal influence on the FC between large-scale networks, including the visual network, default mode network (DMN), frontoparietal network, somatosensory network (SSN), and ventral attention network. Additionally, BN had a causal impact on the within-network FC of both the DMN and SSN. The study provides evidence that BN leads to further changes in FC patterns within and between large-scale brain networks.

Wavepacket interference of two photons through a beam splitter: from temporal entanglement to wavepacket shaping

Quantum interferences based on beam splitting are widely used for entanglement. However, the quantitative measurement of the entanglement in terms of temporal modes and wavepacket shaping facilitated by this entanglement remain unexplored. Here we analytically study the interference of two photons with different temporal shapes through a beam splitter (BS) and then propose its application in temporal entanglement and shaping of photons. The temporal entanglement described by Von Neumann entropy is determined by the splitting ratio of BS and temporal indistinguishability of input photons. We found that maximum mode entanglement can be achieved with a 50/50 BS configuration, enabling the generation of a Bell state encoded in temporal modes, independent of the exact form of the input photons. Then, detecting one of the entangled photons at a specific time enables the probabilistic shaping of the other photon. This process can shape the exponentially decaying (ED) wavepacket into the ED sine shapes, which can be further shaped into Gaussian shapes with a fidelity exceeding 99%. The temporal entanglement and shaping of photons based on interference may solve the shape mismatch issues in large-scale optical quantum networks.

A Spatial-Temporal Aggregated Graph Neural Network for Docked Bike-Sharing Demand Forecasting

Predicting the number of rented and returned bikes at each station is crucial for operators to proactively manage shared bike relocation. Although existing research has proposed spatial-temporal prediction models that significantly advance traffic prediction, these models often neglect the unique characteristics of shared bike systems (BSS). Spatially, the entire bike-sharing system (BSS) experiences peak activity during morning and evening rush hours, whereas, during other periods, activity is localized to local stations, with some recording no rides, highlighting the need to distinguish between global and local spatial information across different times. Temporally, the historical riding records for each station exhibit non-stationary patterns, necessitating the analysis of both global trends and local fluctuations. Existing Graph Neural Network (GNN) approaches to predicting shared bike demand primarily capture static spatial-temporal data and fail to account for the dynamic nature of bike flows. Moreover, these studies focus on global spatial-temporal information without considering local nuances, making it challenging to capture spatiotemporal dynamics in fluctuating BSS. To address these challenges, we introduce the Spatial-Temporal Aggregated Graph Neural Network (STAGNN). Our model first constructs a dynamic adjacent matrix to describe the evolving connections between stations, followed by local and global information layers to capture spatial-temporal information from large-scale shared bike networks accurately. Our methodology has been validated through experiments on four real-world datasets, comparing it against benchmark models to demonstrate superior prediction accuracy. Additionally, we conduct extended experiments on four datasets during the morning and evening rush hours, and the results also affirm the efficacy of the STAGNN in enhancing prediction performance.

Enhancing perceptual, attentional, and working memory demands through variable practice schedules: insights from high-density EEG multi-scale analyses.

Contextual interference (CI) enhances learning by practicing motor tasks in a random order rather than a blocked order. One hypothesis suggests that the benefits arise from enhanced early perceptual/attentional processes, while another posits that better learning is due to highly activated mnemonic processes. We used high-density electroencephalography in a multi-scale analysis approach, including topographic analyses, source estimations, and functional connectivity, to examine the intertwined dynamics of attentional and mnemonic processes within short time windows. We recorded scalp activity from 35 participants as they performed an aiming task at three different distances, under both random and blocked conditions using a crossover design. Our results showed that topographies associated with processes related to perception/attention (N1, P3a) and working memory (P3b) were more pronounced in the random condition. Source estimation analyses supported these findings, revealing greater involvement of the perceptual ventral pathway, anterior cingulate and parietal cortices, along with increased functional connectivity in ventral alpha and frontoparietal theta band networks during random practice. Our results suggest that CI is driven, in the random compared to the blocked condition, by enhanced specific processes such as perceptual, attentional, and working memory processes, as well as large-scale functional networks sustaining more general attentional and executive processes.

Noise analysis of a quasi-phase-matched quantum frequency converter and higher-order counter-propagating SPDC

Quantum frequency conversion (QFC) will be an indispensable ingredient in future quantum technologies. For example, large-scale fibre-based quantum networks will require QFC to interconnect heterogeneous building blocks like emitters, channels, memories, and detectors. The performance of existing QFC devices – typically realized in periodically poled nonlinear crystals – is often severely limited by parasitic noise that arises when the pump wavelength lies between the inter-converted wavelengths. Here we comprehensively investigate the noise spectrum of a QFC device pumped by a CW 1064 nm laser. The converter was realized as a bulk periodically poled potassium titanyl phosphate (ppKTP) crystal quasi-phase-matched for conversion between 637 nm and 1587 nm, which was also polished and coated to resonantly enhance the pump field by a factor of 50. While Raman scattering dominates the noise background from 1140 nm to 1330 nm, at larger energy shifts (beyond 60 THz), parasitic spontaneous parametric down-conversion (SPDC) is the strongest noise source. Further, the noise spectrum was contaminated by a regular succession of narrow-band peaks, which we attribute to a heretofore unidentified higher-order counter-propagating SPDC processes – with quasi-phase-matching orders up to 44 evident in our measurements. This work provides a comprehensive overview of the limiting noise sources in QFC devices that use quasi-phase-matched crystals and will prove an invaluable resource in guiding their future development.