Local Node Research Articles

Maximizing Influence in Social Networks Using Combined Local Features and Deep Learning-Based Node Embedding.

The influence maximization problem has several issues, including low infection rates and high time complexity. Many proposed methods are not suitable for large-scale networks due to their time complexity or free parameter usage. To address these challenges, this article proposes a local heuristic called Embedding Technique for Influence Maximization (ETIM) that uses shell decomposition, graph embedding, and reduction, as well as combined local structural features. The algorithm selects candidate nodes based on their connections among network shells and topological features, reducing the search space and computational overhead. It uses a deep learning-based node embedding technique to create a multidimensional vector of candidate nodes and calculates the dependency on spreading for each node based on local topological features. Finally, influential nodes are identified using the results of the previous phases and newly defined local features. The proposed algorithm is evaluated using the independent cascade model, showing its competitiveness and ability to achieve the best performance in terms of solution quality. Compared with the collective influence global algorithm, ETIM is significantly faster and improves the infection rate by an average of 12%.

Optimizing Virtual Network Embedding for Avionics with Time-Triggered Traffic Scheduling

Network virtualization technology abstracts the nodes and links resources in the physical network, and enables multiple virtual networks (VNs) to share the substrate network (SN) resources through the Virtual Netw ork Embedding (VNE) method. For time-triggered Ethernet (TTE) used in avionics, a time-triggered traffic scheduling oriented virtual network embedding (TT-VNE) method is proposed. While meeting the total resource constraints of traditional VNE problem, it ensures the strict periodicity of time-triggered (TT) traffic. During the solution process, the method sorts the virtual nodes according to the TT traffic bandwidth requirements and the total bandwidth requirements of the links connected to the virtual nodes, embeds the virtual nodes using the breadth first search algorithm, and plans the virtual links in candidate shortest path aggregation. If the TT traffic in the current virtual link is not schedulable, the iterative design of local virtual node re embedding and path planning is carried out. The simulation shows that the request acceptance rate of TT-VNE is not lower than that of existing methods, and when the number of virtual network requests in the star topology exceeds 30 , its request acceptance rate is about 14.3%higher than the VNE-NTANRC-D method which only considers the network topology and resource attributes.

Dynamical state and driving region of combustion instability in a turbulent combustor with a variable swirling flow system

We experimentally study the dynamical state and driving region of combustion instability in a turbulent combustor under low- and high-swirling flow conditions by the complex-systems approach. As the swirl number is increased, the combustion state undergoes a transition from stable combustion to complex combustion instability with large-amplitude modulations via low- and high-amplitude combustion instability. A chaotic state emerges during combustion instability with large-amplitude modulations. A symbolic dynamics-based thermoacoustic power network can identify the emergence of the thermoacoustic power source during combustion instability. The driving region of combustion instability can be clearly extracted from the symbolic dynamics-based synchronization index and the local node entropy in the Rayleigh index-based transition network. Reservoir computing has a potential use in clarifying the phase synchronized state between acoustic pressure and heat release rate fluctuations during the chaotic state in combustion instability with large-amplitude modulations.

Bayesian Federated Learning with Hamiltonian Monte Carlo: Algorithm and Theory

This work introduces a novel and efficient Bayesian federated learning algorithm, namely, the Federated Averaging stochastic Hamiltonian Monte Carlo (FA-HMC), for parameter estimation and uncertainty quantification. We establish rigorous convergence guarantees of FA-HMC on non-iid distributed datasets, under the strong convexity and Hessian smoothness assumptions. Our analysis investigates the effects of parameter space dimension, noise on gradients and momentum, and the frequency of communication (between the central node and local nodes) on the convergence and communication costs of FA-HMC. Beyond that, we establish the tightness of our analysis by showing that the convergence rate cannot be improved even for continuous FA-HMC process. Moreover, extensive empirical studies demonstrate that FA-HMC outperforms the existing Federated Averaging-Langevin Monte Carlo (FA-LD) algorithm. Supplementary materials for this article are available online.

SP6.1 - Use of Endomag (Magseed® and Magtrace®): our initial experience at Midyorks trust

Abstract Introduction WLE is a cornerstone in breast surgery post MILAN trail. Breast lesions undergo wire guided localisation. Drawbacks are scheduling, patient dissatisfaction, wire movement and discomfort, SLNB: Combined technique. Radioactive (TcM99m) labelled nanocolloid and 2mls of diluted Patent V blue dye. NEWSTART and ALMANAC, largest UK SLNB datasets- 1% allergy rate Whereas in endomag, Magseed® Marker- Tiny Magnetic seed Less intrusive than guide-wire- flexible and precise. Can be inserted anytime before surgery. Only becomes detectable when Sentimag Probe passes over it. Magtrace® Lymphatic Tracer Non radioactive and non toxic- safer compared to blue-dye Can be injected up to 28 days prior to surgery. Aim Demonstrate the feasibility of Magseed® marker localisation and Magtrace® tracer injection for breast lesions and sentinel lymph nodes in our unit. Methods All patients who underwent breast WLE from 12/21 to 03/22 using endomag. Sample size identified 63 Total number of procedures: 64 Diagnostic excisions 8 WLE 16 WLE and SNB 39 WLE and ANC 1 Technique of magseed insertion: Stereo 41, U/S 22. Eight patients had clear margin after second surgery One patient needed third surger Magseed not retrieved in original specimen but retrieved via intra-operative shave during same surgery: 4/ 64 (6.25%) Magseed not retrieved during surgery: 3/ 64 (4.68%) Two patients required redo surgery and one completion mastectomy.Magtrace Tracer Failed SNB requiring sampling: 7 / 39 (17.9%). Conclusion Endomag is safe and accurate with comparable surgical endpoints (margins) to traditional localisation. With time (learning curve) intra-operative magseed localisation and sentinel node detection will improve.

Training-free approach to constructing ensemble of local experts

Conventional machine learning methods assume that a central server holds the entire training dataset for training a global model. However, in many real-world situations, datasets are collected from individual local nodes in distributed environments. Gathering these datasets often poses challenges owing to various practical constraints such as data security and privacy issues, storage limits, and transmission costs. When data distributions differ among local nodes, local experts trained on individual local nodes exhibit poor generalization performance for new instances. In this study, we propose a training-free method for constructing an ensemble of local experts without accessing datasets. For a query instance, we apply uncertainty quantification and out-of-distribution (OOD) detection to calculate the uncertainty and OOD scores of the predictions from individual models. We then determine the weights of the models by assigning a lower weight to those with higher uncertainty and OOD scores. Using these weights, the predictions of the individual models are aggregated to obtain a final prediction. In contrast to existing methods that impose specific application requirements, the proposed method can be applied in situations in which only individual predictions from local models are available for use when making a prediction for a query instance. We evaluate the effectiveness of the proposed method using image classification benchmark datasets.

Continuous Dictionary of Nodes Model and Bilinear-Diffusion Representation Learning for Brain Disease Analysis.

Brain networks based on functional magnetic resonance imaging (fMRI) provide a crucial perspective for diagnosing brain diseases. Representation learning has recently attracted tremendous attention due to its strong representation capability, which can be naturally applied to brain disease analysis. However, traditional representation learning only considers direct and local node interactions in original brain networks, posing challenges in constructing higher-order brain networks to represent indirect and extensive node interactions. To address this problem, we propose the Continuous Dictionary of Nodes model and Bilinear-Diffusion (CDON-BD) network for brain disease analysis. The CDON model is innovatively used to learn the original brain network, with its encoder weights directly regarded as latent features. To fully integrate latent features, we further utilize Bilinear Pooling to construct higher-order brain networks. The Diffusion Module is designed to capture extensive node interactions in higher-order brain networks. Compared to state-of-the-art methods, CDON-BD demonstrates competitive classification performance on two real datasets. Moreover, the higher-order representations learned by our method reveal brain regions relevant to the diseases, contributing to a better understanding of the pathology of brain diseases.

Microwave ablation for lymph node metastasis in thyroid cancer: the impact of lymph node diameter.

To explore the impact of lymph node diameter on the efficacy and safety of ultrasound-guided microwave ablation (MWA) in the treatment of cervical metastatic lymph nodes (CMLNs) from thyroid cancer. A total of 32 patients with 58 CMLNs from thyroid cancer underwent ultrasound-guided MWA and were included in the retrospective study. Patients were divided into three groups based on the mean largest diameter of the CMLNs: Group A (diameter ≤10mm), Group B (10mm < diameter ≤20mm), and Group C (diameter >20mm). The research involved comparing changes in cervical metastatic lymph nodes and serum thyroglobulin (sTg) levels, as well as the incidence of complications, before and after microwave ablation across three groups of patients. The technical success rate of this study was 100% (32/32), and they showed no major complications. Compared with measurements taken before MWA, the mean largest diameter and volume of CMLNs, as well as the sTg level, showed significant reductions (p <0.05) at the last follow-up in all three patient groups. Group A and B exhibited higher lymph node volume reduction rates and complete disappearance rates compared to Group C. However, the recurrence rate in the three groups were in the following order: Group C > Group B > Group A. The occurrence rate of mild complications was Group A > Group C > Group B. MWA is a safe and effective method for treating CMLNs, with advantages for localized nodes but limitations for larger ones. Careful consideration and personalized plans are advised, based on comprehensive evidence assessment.

An Efficient Modified Black Widow Optimized Node Localization in Wireless Sensor Network

Wireless Sensor Network (WSN) is an encouraging technology in various applications like atmosphere monitoring, vehicular systems, and targeting devices. Localization in WSN is performed to identify the current position or coordinates of sensor nodes. The crucial task of localization is to find coordinates without manual configuration and expensive hardware like GPS. This work proposes a new localization algorithm using Modified Black Widow Optimization (MBWO). The proposed method involves an optimal selection of anchor nodes to reduce localization errors. Black Widow Optimization (BWO) is a naturally inspired optimization algorithm encouraged by the unique coupling behaviour of black widow spiders. The performance of black widow optimization is improved by levy flight distribution to escape from local minima. Experimental results show that the proposed MBWO-optimized localization achieves a higher localized node count with minimized localized error compared to other optimization algorithms.

LSGDDN-LCD: An appearance-based loop closure detection using local superpixel grid descriptors and incremental dynamic nodes

Loop Closure Detection (LCD) is an essential component of visual Simultaneous Localization and Mapping (SLAM) systems. It enables the recognition of previously visited scenes to eliminate pose and map estimate drifts arising from long-term exploration. However, current appearance-based LCD methods face significant challenges, including high computational costs, viewpoint changes, and dynamic objects in scenes. This paper introduced an online appearance based LCD using Local Superpixel Grids Descriptor (LSGD) and Dynamic Nodes (DN), i.e., LSGDDN-LCD, to find similarities between scenes via handcrafted features extracted from the LSGD. Additionally, we proposed the adaptive mechanism to group similar scenes called DynamicNodes, which incrementally adjusted the database in an online manner, allowing for efficient and online retrieval of previously viewed images without need of the pre-training. Experimental results confirmed that the LSGDDN-LCD significantly improved LCD precision–recall and efficiency, and outperformed several state-of-the-art (SOTA) approaches on public and our own datasets, indicating its great potential as a generic LCD framework. Our implementation of the LSGDDN-LCD approach and the datasets were open-sourced on GitHub (https://github.com/BaoshengZhang0/LSGDDN-LCD.git).

Graph isomorphism network with weighted multi‐aggregators for building shape classification

AbstractBuilding shape cognition is essential for tasks, such as map generalization, urban modeling, and building semantics and distribution pattern recognition. Traditional geometric and statistical methods rely on human‐defined shape indicators, and spectral‐based graph neural networks (GNNs) require Laplacian eigendecomposition, resulting in high algorithmic complexity. Therefore, we proposed a low‐complexity and simple‐to‐use spatial‐domain GNN for differentiating building shapes. To examine the influence of the building vertices on their shape, we treated each building as a graph and proposed a graph isomorphic network with weighted multi‐aggregators (GIN‐WMA) by analyzing the node connectivity of a building graph. The GIN‐WMA utilizes a novel aggregator that combines the sum and max aggregators, enhancing its recognition and differentiation capabilities. This approach can effectively differentiate nodes that have identical features after aggregation by the sum aggregator. We extracted features considering both local node and global shape features, drawing inspiration from Gestalt cognitive psychology and GNN's “node–graph” differentiation strategy. In addition, we compared the performance of GIN‐WMA with existing methods, studying the effect of various node features and their combinations on classification accuracy. The results demonstrated that GIN‐WMA outperforms other methods in discriminating building shapes, demonstrating superior capabilities in shape classification and enabling end‐to‐end extraction and classification of building shapes.

Research on key technologies for automatic planning of multiple links on a single node

Abstract Aiming at the problems of slow speed and low efficiency existing in the traditional manual planning method of multiple links on a single node, which cannot fully meet the rapid deployment needs of communication nodes, this paper studies and puts forward two automatic planning technologies of single-node-multi-link based on shortest distance priority and minimum angle priority. This is an automatic planning technology that uses the shortest distance priority to determine the pairing relationship between the multi-scattering antenna of the local communication node and the far-end scattering antenna. It aims to solve the issue of cross-redundancy of distance in wrong planning. Automatic planning technology of single-node-multi-link based on minimum angle priority simulates manual planning mode. Using the concept of minimum angle priority, the pairing relationship between the multi-scattering antenna of the local communication node and the remote scattering antenna is determined. This is achieved by calculating and optimizing the minimum angle of two rays that are formed from the origin of the coordinate system of the local communication node to the local scattering antenna and the remote scattering antenna, respectively. Comparative analysis and simulation experiments prove that the two key technologies proposed in this paper can not only effectively avoid the problem of mutual interference between multiple scattering antennas in this node but also improve the efficiency of multi-link planning on a single node.

Integrated Structural Hole and K-shell Algorithm for Tsallis Entropy-based Identification of Key Nodes in Power Grids

Abstract Considering the low-carbon development goals of “peak carbon emissions and carbon neutrality,” traditional energy enterprises, including oil fields, have accelerated the incorporation of new energy into their power grids. However, incorporating new energy generation into traditional oilfield power grids yields a series of safety hazards, making the stability of the oilfield power grid structure increasingly important. In this paper, a redefined theory of structural holes and the K-shell algorithm are utilized to identify both local and global key nodes in the oilfield power grid. The improved Tsallis entropy is employed to recognize these key nodes, accounting for their local influence within the oilfield power grid as well as their global status. Additionally, considering the electrical characteristics of the nodes, a set of measurement metrics suitable for oilfield power grid research is constructed. Finally, the IEEE-39 feeder system is simulated and compared with other key node identification methods. By analyzing the robustness of the topological structure and the loss load value of the power system after removing key nodes, the reliability and superiority of the proposed method are verified.

An energy efficient clustering algorithm based on density and fitness for mobile crowd-sensing network

Mobile crowd-sensing (MCS) is a cutting-edge paradigm that gathers sensory data and generates valuable insights for a multitude of users by utilizing built-in sensors and social applications in mobile devices. This enables a broad spectrum of Internet of Things (IoT) services. We introduce a novel MCS algorithm, Mobile Crowd-sensing Low Energy Clustering (MCLEC), which employs advanced clustering techniques to address issues of data oversampling and energy inefficiency prevalent in MCS networks. MCLEC innovatively adjusts clustering radii based on local node density and the proximity of nodes to the cloud server, thus optimizing data transmission paths and reducing energy consumption. A pivotal enhancement in MCLEC is its cluster head election strategy, which prioritizes leaders based on their energy levels and mobility, thereby enhancing network stability and minimizing the frequency of head re-elections. Our comparisons with established algorithms such as LEACH, LEACH-C, LEACH-M, DEEC, and SEP show that MCLEC significantly improves energy efficiency, reduces server load, and prolongs the lifespan of network nodes, establishing it as an effective solution for IoT applications dependent on MCS. Additionally, MCLEC was compared with other novel clustering algorithms including E-FLZSEPFCH, DFLC, ECPF, ACAWT, UCR, CHEF, and Gupta's algorithm. The results indicate that MCLEC also surpasses most of these algorithms in terms of energy consumption and network lifetime.

Generic network sparsification via degree- and subgraph-based edge sampling

Network (or graph) sparsification accelerates many downstream analyses. For graph sparsification, sampling methods derived from local heuristic considerations are common in practice, due to their efficiency in generating sparse subgraphs using only local information. Filtering-based edge sampling is the most typical approach in this respect, yet it heavily depends on an appropriate definition of edge importance. Instead, we propose a generalized node-focused edge sampling framework by preserving scaled/expected local node characteristics. Apart from expected degrees, these local node characteristics include the expected number of triangles and the expected number of non-closed wedges associated with a node. From a technical point of view, we adapt a game-theoretic sampling method from uncertain graph generation to obtain sparse subgraphs that approximate the expected local properties. We include a tolerance threshold for much faster convergence. Within this framework, we provide appropriate algorithmic variants for sparsification. Moreover, we propose a network measure called tri-wedge assortativity for the selection of the most suitable variant when sparsifying a given network. Extensive experimental studies on functional climate, observed real-world, and synthetic networks show the effectiveness of our method in preserving overall structural network properties – on average consistently better than the state of the art.

An unsupervised deep global–local views model for anomaly detection in attributed networks

An attribute network is a form of data that contains rich semantic information. Many real scenarios can be modeled as attributed networks, such as social media, citations, and traffic networks. Anomaly detection in attributed networks is an interesting research topic owing to its potential in various practical applications, including spam, network intrusion, and financial fraud detection. However, attributed networks exhibit many anomaly patterns, such as structural, attribute, local, and global anomalies, making anomaly detection in attributed networks a challenging task. To address these difficulties, we designed DeepGL, a novel unsupervised deep global–local view model, for anomaly detection in attributed networks. Our model is an encoder–decoder framework with multiple views that capture node attributes and network structure information from both global and local views. Specifically, our model contains two encoders and four decoders. The two encoders are used to capture network features from local and global views, and the four decoders are used to reconstruct the local node attribute information, local structure information, global node attribute information, and global structure information. To the encoders and decoders, we applied Laplacian sharpening and smoothing techniques to maintain the integrity of normal node features while diminishing the conspicuousness of anomalous nodes in the reconstructed information, thereby facilitating the calculation of reconstruction errors. Extensive experiments on four real-world attributed network datasets demonstrate the excellent performance of the proposed method.

Energy balancing strategy for the multi-storage islanded DC microgrid based on hierarchical cooperative control

To simultaneously solve the problems of the state-of-charge (SOC) equalization and accurate current distribution among distributed energy storage units (DESUs) with different capacities in isolated DC microgrids, a multi-storage DC microgrid energy equalization strategy based on the hierarchical cooperative control is proposed. In the primary control layer, the link between the droop coefficient and SOC is established through a logarithmic function, and the droop coefficient is adaptively adjusted according to the SOC value in order to achieve fast SOC equalization. In the secondary control layer, by designing a coordinated state factor, only one PI controller is required to eliminate the influence of the line impedance on the accurate distribution of the output current and the DC bus voltage drop. In the communication layer, local nodes only need to communicate with neighboring nodes without the need for a central controller, and the dynamic consistency algorithm is used to obtain the average value information about the energy storage system (ESS). Finally, the feasibility and effectiveness of the proposed control strategy are verified by experimental analysis on the DC microgrid hardware-in-the-loop experimental platform.

Hybrid spatial network disintegration strategy considering geographic and topological information

Abstract When considering disintegration of spatial networks, the topological relationships between nodes and their geographical positions are taken into account. In the case of regional attacks, the targeted nodes are determined based on the size of the region and the geographical relationships between nodes. In this paper, a new metric is proposed on top of region centrality (RC) to describe the importance of nodes within local regions in spatial networks. We define the weighted sum of the number of connections between nodes within a local region and the number of connections between nodes within the local region and nodes outside the local region as local connectivity. This metric integrates topological information with geographical information. From a mathematical perspective, RC is a special case of local connectivity. In addition, we propose a hybrid spatial network disintegration strategy based on two metrics, namely local connectivity and RC. From experimental results, our strategy outperforms traditional approaches and demonstrates a stronger ability to completely disintegrate the network.

LMFLS: A new fast local multi-factor node scoring and label selection-based algorithm for community detection

Community detection is still regarded as one of the most applicable approaches for discovering latent information in complex networks. To meet the needs of processing large networks in today's world, it is important to propose fast methods that have low execution time and fast convergence speed, while maintaining algorithmic accuracy. To overcome these issues, a fast local multi-factor node scoring and label selection-based (LMFLS) method with low time complexity and fast convergence is proposed. Node scoring step incorporates diverse metrics to better assess impact of nodes from different aspects and obtain more meaningful order of nodes. In second step, to construct and stabilize initial structure of communities, an efficient label assignment technique based on the selection of the most similar neighbor is suggested. Moreover, two label selection strategies are proposed to significantly enhance the accuracy and improve convergence of the algorithm. During the label selection step, each node in graph tends to choose the most appropriate label based on a multi-criteria label influence from its surrounding nodes. Finally, by utilizing a novel merge method, small group of nodes are merged to form the final communities. Meanwhile, since drug repositioning is one of the popular research fields in therapeutics, to extend the application of the proposed algorithm in practical context, the LMFLS algorithm is applied on Drug-Drug network to find potential repositioning for drugs. Thorough experiments are conducted on both actual real-world networks and synthetic networks to assess the algorithm's performance and accuracy. The findings demonstrate that the proposed method outperforms state-of-the-art algorithms in terms of both accuracy and execution time.

Establishment and optimization of urban ecological network based on ecological regulation services aiming at stability and connectivity

Establishing an urban ecological network is a means to improve the ecological environment and enhance sustainable urban development amidst rapid urbanization. Based on the demand of eco-city, morphological spatial pattern analysis (MSPA), ecological regulation services value (ERSV), and landscape connectivity have been combined to identify ecological resources with high connectivity and ecological benefits in this study. Using the Minimum Cumulative Resistance (MCR) model, ecological corridors have been constructed and ecological critical nodes have been identified. In addition, the urban ecological network has then been optimized from the overall layout and local key nodes based on the circuit theory to balance the ecological livability and species diversity in the eco-city. The main conclusions could be demonstrated as follows: (1) 76.45% of the high ecological regulation service value areas have been identified as core areas. (2) The distribution of ecological corridors has been found uneven with significant disparities (3) The connectivity indices, especially the line point rate (β) index, have increased by 21.66%, while the robustness has increased by 75.44%. Overall, the combined methods have been used to establish and optimize the ecological network within the limited space to balance the ecological livability and biodiversity. The results of this study could provide a scientific reference for the optimization of urban ecological space and promote sustainable development in the eco-city.