Accurately identifying key nodes is essential for evaluating network robustness and controlling information propagation in complex network analysis. However, current research methods face limitations in applicability ...Accurately identifying key nodes is essential for evaluating network robustness and controlling information propagation in complex network analysis. However, current research methods face limitations in applicability and accuracy. To address these challenges, this study introduces the K-GCN model, which integrates neighborhood k-shell distribution analysis with Graph Convolutional Network(GCN) technology to enhance key node identification in complex networks. The K-GCN model first leverages neighborhood k-shell distributions to calculate entropy values for each node, effectively quantifying node importance within the network. These entropy values are then used as key features within the GCN, which subsequently formulates intelligent strategies to maximize network connectivity disruption by removing a minimal set of nodes, thereby impacting the overall network architecture. Through iterative interactions with the environment, the GCN continuously refines its strategies, achieving precise identification of key nodes in the network. Unlike traditional methods, the K-GCN model not only captures local node features but also integrates the network structure and complex interrelations between neighboring nodes, significantly improving the accuracy and efficiency of key node identification.Experimental validation in multiple real-world network scenarios demonstrates that the K-GCN model outperforms existing methods.展开更多
Background With the development of the Internet,the topology optimization of wireless sensor networks has received increasing attention.However,traditional optimization methods often overlook the energy imbalance caus...Background With the development of the Internet,the topology optimization of wireless sensor networks has received increasing attention.However,traditional optimization methods often overlook the energy imbalance caused by node loads,which affects network performance.Methods To improve the overall performance and efficiency of wireless sensor networks,a new method for optimizing the wireless sensor network topology based on K-means clustering and firefly algorithms is proposed.The K-means clustering algorithm partitions nodes by minimizing the within-cluster variance,while the firefly algorithm is an optimization algorithm based on swarm intelligence that simulates the flashing interaction between fireflies to guide the search process.The proposed method first introduces the K-means clustering algorithm to cluster nodes and then introduces a firefly algorithm to dynamically adjust the nodes.Results The results showed that the average clustering accuracies in the Wine and Iris data sets were 86.59%and 94.55%,respectively,demonstrating good clustering performance.When calculating the node mortality rate and network load balancing standard deviation,the proposed algorithm showed dead nodes at approximately 50 iterations,with an average load balancing standard deviation of 1.7×10^(4),proving its contribution to extending the network lifespan.Conclusions This demonstrates the superiority of the proposed algorithm in significantly improving the energy efficiency and load balancing of wireless sensor networks to extend the network lifespan.The research results indicate that wireless sensor networks have theoretical and practical significance in fields such as monitoring,healthcare,and agriculture.展开更多
Healthcare networks are transitioning from manual records to electronic health records,but this shift introduces vulnerabilities such as secure communication issues,privacy concerns,and the presence of malicious nodes...Healthcare networks are transitioning from manual records to electronic health records,but this shift introduces vulnerabilities such as secure communication issues,privacy concerns,and the presence of malicious nodes.Existing machine and deep learning-based anomalies detection methods often rely on centralized training,leading to reduced accuracy and potential privacy breaches.Therefore,this study proposes a Blockchain-based-Federated Learning architecture for Malicious Node Detection(BFL-MND)model.It trains models locally within healthcare clusters,sharing only model updates instead of patient data,preserving privacy and improving accuracy.Cloud and edge computing enhance the model’s scalability,while blockchain ensures secure,tamper-proof access to health data.Using the PhysioNet dataset,the proposed model achieves an accuracy of 0.95,F1 score of 0.93,precision of 0.94,and recall of 0.96,outperforming baseline models like random forest(0.88),adaptive boosting(0.90),logistic regression(0.86),perceptron(0.83),and deep neural networks(0.92).展开更多
In a post-disaster environment characterized by frequent interruptions in communication links,traditional wireless communication networks are ineffective.Although the“store-carry-forward”mechanism characteristic of ...In a post-disaster environment characterized by frequent interruptions in communication links,traditional wireless communication networks are ineffective.Although the“store-carry-forward”mechanism characteristic of Delay Tolerant Networks(DTNs)can transmit data from Internet of things devices to more reliable base stations or data centres,it also suffers from inefficient data transmission and excessive transmission delays.To address these challenges,we propose an intelligent routing strategy based on node sociability for post-disaster emergency network scenarios.First,we introduce an intelligent routing strategy based on node intimacy,which selects more suitable relay nodes and assigns the corresponding number of message copies based on comprehensive utility values.Second,we present an intelligent routing strategy based on geographical location of nodes to forward message replicas secondarily based on transmission utility values.Finally,experiments demonstrate the effectiveness of our proposed algorithm in terms of message delivery rate,network cost ratio and average transmission delay.展开更多
This paper studies the target controllability of multilayer complex networked systems,in which the nodes are highdimensional linear time invariant(LTI)dynamical systems,and the network topology is directed and weighte...This paper studies the target controllability of multilayer complex networked systems,in which the nodes are highdimensional linear time invariant(LTI)dynamical systems,and the network topology is directed and weighted.The influence of inter-layer couplings on the target controllability of multi-layer networks is discussed.It is found that even if there exists a layer which is not target controllable,the entire multi-layer network can still be target controllable due to the inter-layer couplings.For the multi-layer networks with general structure,a necessary and sufficient condition for target controllability is given by establishing the relationship between uncontrollable subspace and output matrix.By the derived condition,it can be found that the system may be target controllable even if it is not state controllable.On this basis,two corollaries are derived,which clarify the relationship between target controllability,state controllability and output controllability.For the multi-layer networks where the inter-layer couplings are directed chains and directed stars,sufficient conditions for target controllability of networked systems are given,respectively.These conditions are easier to verify than the classic criterion.展开更多
Ethereum, currently the most widely utilized smart contracts platform, anchors the security of myriad smartcontracts upon its own robustness. Its foundational peer-to-peer network facilitates a dependable node connect...Ethereum, currently the most widely utilized smart contracts platform, anchors the security of myriad smartcontracts upon its own robustness. Its foundational peer-to-peer network facilitates a dependable node connectionmechanism, whereas an efficient data-sharing protocol constitutes as the bedrock of Blockchain network security.In this paper, we propose NodeHunter, an Ethereum network detector implemented through the application ofsimulation technology, which is capable of aggregating all node records within the network and the interconnectednessbetween them. Utilizing this connection information, NodeHunter can procure more comprehensive insightsfor network status analysis compared to preceding detection methodologies. Throughout a three-month period ofunbroken surveillance of the Ethereum network, we obtained an excess of two million node records along with overone hundred million node acquaintances. Analysis of the gathered data revealed that an alarming 49% or more ofthese node records were maliciously forged.展开更多
As industrialization and informatization becomemore deeply intertwined,industrial control networks have entered an era of intelligence.The connection between industrial control networks and the external internet is be...As industrialization and informatization becomemore deeply intertwined,industrial control networks have entered an era of intelligence.The connection between industrial control networks and the external internet is becoming increasingly close,which leads to frequent security accidents.This paper proposes a model for the industrial control network.It includes a malware containment strategy that integrates intrusion detection,quarantine,and monitoring.Basedonthismodel,the role of keynodes in the spreadofmalware is studied,a comparisonexperiment is conducted to validate the impact of the containment strategy.In addition,the dynamic behavior of the model is analyzed,the basic reproduction number is computed,and the disease-free and endemic equilibrium of the model is also obtained by the basic reproduction number.Moreover,through simulation experiments,the effectiveness of the containment strategy is validated,the influence of the relevant parameters is analyzed,and the containment strategy is optimized.In otherwords,selective immunity to key nodes can effectively suppress the spread ofmalware andmaintain the stability of industrial control systems.The earlier the immunization of key nodes,the better.Once the time exceeds the threshold,immunizing key nodes is almost ineffective.The analysis provides a better way to contain the malware in the industrial control network.展开更多
Aiming at the problem that it is difficult to build model and identify the vulnerable equipment for aviation armament System-of-Systems(SoS)due to complex equipment interaction relationships and high confrontation,the...Aiming at the problem that it is difficult to build model and identify the vulnerable equipment for aviation armament System-of-Systems(SoS)due to complex equipment interaction relationships and high confrontation,the interdependent network theory is introduced to solve it.Firstly,a two-layer heterogeneous interdependent network model for aviation armament SoS is proposed,which reflects the information interaction,functional dependency and inter-network dependence effectively.Secondly,using the attack cost to describe the confrontation process and taking the comprehensive impact on kill chains as the entry point,the node importance index and the attack cost measurement method are constructed.Thirdly,the identification of vulnerable nodes is transformed into the optimization problem of node combinatorial selection,and the vulnerable node identification method based on tabu search is proposed.Based on vulnerable nodes,a robustness enhancement strategy for aviation armament SoS network is presented.Finally,the above methods are used to an aerial confrontation SoS,and the results verify the rationality and effectiveness of the proposed methods.展开更多
In many engineering networks, only a part of target state variables are required to be estimated.On the other hand,multi-layer complex network exists widely in practical situations.In this paper, the state estimation ...In many engineering networks, only a part of target state variables are required to be estimated.On the other hand,multi-layer complex network exists widely in practical situations.In this paper, the state estimation of target state variables in multi-layer complex dynamical networks with nonlinear node dynamics is studied.A suitable functional state observer is constructed with the limited measurement.The parameters of the designed functional observer are obtained from the algebraic method and the stability of the functional observer is proven by the Lyapunov theorem.Some necessary conditions that need to be satisfied for the design of the functional state observer are obtained.Different from previous studies, in the multi-layer complex dynamical network with nonlinear node dynamics, the proposed method can estimate the state of target variables on some layers directly instead of estimating all the individual states.Thus, it can greatly reduce the placement of observers and computational cost.Numerical simulations with the three-layer complex dynamical network composed of three-dimensional nonlinear dynamical nodes are developed to verify the effectiveness of the method.展开更多
This paper studies the controllability of networked systems,in which the nodes are heterogeneous high-dimensional dynamical systems,and the links between nodes are multi-relational.Our aim is to find controllability c...This paper studies the controllability of networked systems,in which the nodes are heterogeneous high-dimensional dynamical systems,and the links between nodes are multi-relational.Our aim is to find controllability criteria for heterogeneous networks with multi-relational links beyond those only applicable to networks with single-relational links.It is found a network with multi-relational links can be controllable even if each single-relational network topology is uncontrollable,and vice versa.Some sufficient and necessary conditions are derived for the controllability of multi-relational networks with heterogeneous dynamical nodes.For two typical multi-relational networks with star-chain topology and star-circle topology,some easily verified conditions are presented.For illustration and verification,several examples are presented.These findings provide practical insights for the analysis and control of multi-relational complex systems.展开更多
Identifying key nodes in complex networks is crucial for understanding and controlling their dynamics. Traditional centrality measures often fall short in capturing the multifaceted roles of nodes within these network...Identifying key nodes in complex networks is crucial for understanding and controlling their dynamics. Traditional centrality measures often fall short in capturing the multifaceted roles of nodes within these networks. The Page Rank algorithm, widely recognized for ranking web pages, offers a more nuanced approach by considering the importance of connected nodes. However, existing methods generally overlook the geometric properties of networks, which can provide additional insights into their structure and functionality. In this paper, we propose a novel method named Curv-Page Rank(C-PR), which integrates network curvature and Page Rank to identify influential nodes in complex networks. By leveraging the geometric insights provided by curvature alongside structural properties, C-PR offers a more comprehensive measure of a node's influence. Our approach is particularly effective in networks with community structures, where it excels at pinpointing bridge nodes critical for maintaining connectivity and facilitating information flow. We validate the effectiveness of C-PR through extensive experiments. The results demonstrate that C-PR outperforms traditional centrality-based and Page Rank methods in identifying critical nodes. Our findings offer fresh insights into the structural importance of nodes across diverse network configurations, highlighting the potential of incorporating geometric properties into network analysis.展开更多
In order to reveal the complex network characteristics and evolution principle of China aviation network,the relationship between the node degree and the average path length of China aviation network in 1988,1994,2001...In order to reveal the complex network characteristics and evolution principle of China aviation network,the relationship between the node degree and the average path length of China aviation network in 1988,1994,2001,2008 and 2015 was studied.According to the theory and method of complex network,the network system was constructed with the city where the airport was located as the network node and the airline as the edge of the network.On the basis of the statistical data,the node average path length of China aviation network in 1988,1994,2001,2008 and 2015 was calculated.Through regression analysis,it was found that the node degree had a logarithmic relationship with the average length of node path,and the two parameters of the logarithmic relationship had linear evolutionary trace.Key word:China aviation network,complex network,node degree,average length of node path,logarithmic relationship,evolutionary trace.展开更多
The use of dynamic programming(DP)algorithms to learn Bayesian network structures is limited by their high space complexity and difficulty in learning the structure of large-scale networks.Therefore,this study propose...The use of dynamic programming(DP)algorithms to learn Bayesian network structures is limited by their high space complexity and difficulty in learning the structure of large-scale networks.Therefore,this study proposes a DP algorithm based on node block sequence constraints.The proposed algorithm constrains the traversal process of the parent graph by using the M-sequence matrix to considerably reduce the time consumption and space complexity by pruning the traversal process of the order graph using the node block sequence.Experimental results show that compared with existing DP algorithms,the proposed algorithm can obtain learning results more efficiently with less than 1%loss of accuracy,and can be used for learning larger-scale networks.展开更多
Graph neural networks(GNNs)have demonstrated excellent performance in graph representation learning.However,as the volume of graph data grows,issues related to cost and efficiency become increasingly prominent.Graph d...Graph neural networks(GNNs)have demonstrated excellent performance in graph representation learning.However,as the volume of graph data grows,issues related to cost and efficiency become increasingly prominent.Graph distillation methods address this challenge by extracting a smaller,reduced graph,ensuring that GNNs trained on both the original and reduced graphs show similar performance.Existing methods,however,primarily optimize the feature matrix of the reduced graph and rely on correlation information from GNNs,while neglecting the original graph’s structure and redundant nodes.This often results in a loss of critical information within the reduced graph.To overcome this limitation,we propose a graph distillation method guided by network symmetry.Specifically,we identify symmetric nodes with equivalent neighborhood structures and merge them into“super nodes”,thereby simplifying the network structure,reducing redundant parameter optimization and enhancing training efficiency.At the same time,instead of relying on the original node features,we employ gradient descent to match optimal features that align with the original features,thus improving downstream task performance.Theoretically,our method guarantees that the reduced graph retains the key information present in the original graph.Extensive experiments demonstrate that our approach achieves significant improvements in graph distillation,exhibiting strong generalization capability and outperforming existing graph reduction methods.展开更多
Aiming at node deployment in the monitoring area of the field observation instrument network in the cold and arid regions,we propose a virtual force algorithm based on Voronoi diagram(VFAVD),which adopts probabilistic...Aiming at node deployment in the monitoring area of the field observation instrument network in the cold and arid regions,we propose a virtual force algorithm based on Voronoi diagram(VFAVD),which adopts probabilistic sensing model that is more in line with the actual situation.First,the Voronoi diagram is constructed in the monitoring area to determine the Thiessen polygon of each node.Then,the virtual force on each node is calculated,and the node update its position according to the direction and size of the total force,so as to achieve the purpose of improving the network coverage rate.The simulation results show that the proposed algorithm can effectively improve the coverage rate of the network,and also has a good effect on the coverage uniformity.展开更多
As a key mode of transportation, urban metro networks have significantly enhanced urban traffic environments and travel efficiency, making the identification of critical stations within these networks increasingly ess...As a key mode of transportation, urban metro networks have significantly enhanced urban traffic environments and travel efficiency, making the identification of critical stations within these networks increasingly essential. This study presents a novel integrated topological-functional(ITF) algorithm for identifying critical nodes, combining topological metrics such as K-shell decomposition, node information entropy, and neighbor overlapping interaction with the functional attributes of passenger flow operations, while also considering the coupling effects between metro and bus networks. Using the Chengdu metro network as a case study, the effectiveness of the algorithm under different conditions is validated.The results indicate significant differences in passenger flow patterns between working and non-working days, leading to varying sets of critical nodes across these scenarios. Moreover, the ITF algorithm demonstrates a marked improvement in the accuracy of critical node identification compared to existing methods. This conclusion is supported by the analysis of changes in the overall network structure and relative global operational efficiency following targeted attacks on the identified critical nodes. The findings provide valuable insight into urban transportation planning, offering theoretical and practical guidance for improving metro network safety and resilience.展开更多
Wireless Sensor Networks(WSNs)play a critical role in automated border surveillance systems,where continuous monitoring is essential.However,limited energy resources in sensor nodes lead to frequent network failures a...Wireless Sensor Networks(WSNs)play a critical role in automated border surveillance systems,where continuous monitoring is essential.However,limited energy resources in sensor nodes lead to frequent network failures and reduced coverage over time.To address this issue,this paper presents an innovative energy-efficient protocol based on deep Q-learning(DQN),specifically developed to prolong the operational lifespan of WSNs used in border surveillance.By harnessing the adaptive power of DQN,the proposed protocol dynamically adjusts node activity and communication patterns.This approach ensures optimal energy usage while maintaining high coverage,connectivity,and data accuracy.The proposed system is modeled with 100 sensor nodes deployed over a 1000 m×1000 m area,featuring a strategically positioned sink node.Our method outperforms traditional approaches,achieving significant enhancements in network lifetime and energy utilization.Through extensive simulations,it is observed that the network lifetime increases by 9.75%,throughput increases by 8.85%and average delay decreases by 9.45%in comparison to the similar recent protocols.It demonstrates the robustness and efficiency of our protocol in real-world scenarios,highlighting its potential to revolutionize border surveillance operations.展开更多
Graph Neural Networks(GNNs)have demonstrated outstanding capabilities in processing graph-structured data and are increasingly being integrated into large-scale pre-trained models,such as Large Language Models(LLMs),t...Graph Neural Networks(GNNs)have demonstrated outstanding capabilities in processing graph-structured data and are increasingly being integrated into large-scale pre-trained models,such as Large Language Models(LLMs),to enhance structural reasoning,knowledge retrieval,and memory management.The expansion of their application scope imposes higher requirements on the robustness of GNNs.However,as GNNs are applied to more dynamic and heterogeneous environments,they become increasingly vulnerable to real-world perturbations.In particular,graph data frequently encounters joint adversarial perturbations that simultaneously affect both structures and features,which are significantly more challenging than isolated attacks.These disruptions,caused by incomplete data,malicious attacks,or inherent noise,pose substantial threats to the stable and reliable performance of traditional GNN models.To address this issue,this study proposes the Dual-Shield Graph Neural Network(DSGNN),a defense model that simultaneously mitigates structural and feature perturbations.DSGNN utilizes two parallel GNN channels to independently process structural noise and feature noise,and introduces an adaptive fusion mechanism that integrates information from both pathways to generate robust node representations.Theoretical analysis demonstrates that DSGNN achieves a tighter robustness boundary under joint perturbations compared to conventional single-channel methods.Experimental evaluations across Cora,CiteSeer,and Industry datasets show that DSGNN achieves the highest average classification accuracy under various adversarial settings,reaching 81.24%,71.94%,and 81.66%,respectively,outperforming GNNGuard,GCN-Jaccard,GCN-SVD,RGCN,and NoisyGNN.These results underscore the importance of multi-view perturbation decoupling in constructing resilient GNN models for real-world applications.展开更多
Most papillary thyroid carcinoma(PTC) patients have a good prognosis. However, lymph node metastasis(LNM), the most common manifestation of disease progression, is frequently associated with a poor prognosis.Neverthel...Most papillary thyroid carcinoma(PTC) patients have a good prognosis. However, lymph node metastasis(LNM), the most common manifestation of disease progression, is frequently associated with a poor prognosis.Nevertheless, few studies have focused on the underlying mechanisms of LNM. In the current study, we aimed to investigate the potential role of exosomal circRNAs that contribute to LNM in PTC. We identified 9 000 differentially expressed exosomal circRNAs in PTC patients with LNM, including 684 upregulated and 2 193 downregulated circRNAs. Functional enrichment analysis revealed that these differentially expressed circRNAs were primarily involved in a variety of molecular and signaling pathways correlated with PTC progression and LNM. Through bioinformatics analysis, we identified 14 circRNA-miRNA-mRNA networks related to LNM-associated signaling pathways in PTC. Moreover, both circTACC2-miR-7-EGFR and circBIRC6-miR-24-3p-BCL2L11 axes were verified for their potential involvement in PTC with LNM. Additionally, we identified four upregulated circRNA-related hub genes and eight hub genes correlated with downregulated circRNAs, some of which were validated as being potentially involved in LNM in PTC. Collectively, our findings provide a novel framework for an in-depth investigation of the function of dysregulated exosomal circRNAs and their potential as biomarkers in PTC patients with LNM.展开更多
Essential proteins are an indispensable part of cells and play an extremely significant role in genetic disease diagnosis and drug development.Therefore,the prediction of essential proteins has received extensive atte...Essential proteins are an indispensable part of cells and play an extremely significant role in genetic disease diagnosis and drug development.Therefore,the prediction of essential proteins has received extensive attention from researchers.Many centrality methods and machine learning algorithms have been proposed to predict essential proteins.Nevertheless,the topological characteristics learned by the centrality method are not comprehensive enough,resulting in low accuracy.In addition,machine learning algorithms need sufficient prior knowledge to select features,and the ability to solve imbalanced classification problems needs to be further strengthened.These two factors greatly affect the performance of predicting essential proteins.In this paper,we propose a deep learning framework based on temporal convolutional networks to predict essential proteins by integrating gene expression data and protein-protein interaction(PPI)network.We make use of the method of network embedding to automatically learn more abundant features of proteins in the PPI network.For gene expression data,we treat it as sequence data,and use temporal convolutional networks to extract sequence features.Finally,the two types of features are integrated and put into the multi-layer neural network to complete the final classification task.The performance of our method is evaluated by comparing with seven centrality methods,six machine learning algorithms,and two deep learning models.The results of the experiment show that our method is more effective than the comparison methods for predicting essential proteins.展开更多
基金Supported by the National Natural Science Foundation of China(Grant No.12031002)。
文摘Accurately identifying key nodes is essential for evaluating network robustness and controlling information propagation in complex network analysis. However, current research methods face limitations in applicability and accuracy. To address these challenges, this study introduces the K-GCN model, which integrates neighborhood k-shell distribution analysis with Graph Convolutional Network(GCN) technology to enhance key node identification in complex networks. The K-GCN model first leverages neighborhood k-shell distributions to calculate entropy values for each node, effectively quantifying node importance within the network. These entropy values are then used as key features within the GCN, which subsequently formulates intelligent strategies to maximize network connectivity disruption by removing a minimal set of nodes, thereby impacting the overall network architecture. Through iterative interactions with the environment, the GCN continuously refines its strategies, achieving precise identification of key nodes in the network. Unlike traditional methods, the K-GCN model not only captures local node features but also integrates the network structure and complex interrelations between neighboring nodes, significantly improving the accuracy and efficiency of key node identification.Experimental validation in multiple real-world network scenarios demonstrates that the K-GCN model outperforms existing methods.
基金Supported by 2021 Zhanjiang University of Science and Technology"Brand Enhancement Plan"Project:Network Series Course Teaching Team(PPJH202102JXTD)2022 Zhanjiang University of Science and Technology"Brand Enhancement Plan"Project:Network Engineering(PPJHKCSZ-2022301)+1 种基金2023 Zhanjiang Science and Technology Bureau Project:Design and Simulation of Zhanjiang Mangrove Wetland Monitoring Network System(2023B01017)2022 Zhanjiang University of Science and Technology Quality Engineering Project:Audiovisual Language Teaching and Research Office(ZLGC202203).
文摘Background With the development of the Internet,the topology optimization of wireless sensor networks has received increasing attention.However,traditional optimization methods often overlook the energy imbalance caused by node loads,which affects network performance.Methods To improve the overall performance and efficiency of wireless sensor networks,a new method for optimizing the wireless sensor network topology based on K-means clustering and firefly algorithms is proposed.The K-means clustering algorithm partitions nodes by minimizing the within-cluster variance,while the firefly algorithm is an optimization algorithm based on swarm intelligence that simulates the flashing interaction between fireflies to guide the search process.The proposed method first introduces the K-means clustering algorithm to cluster nodes and then introduces a firefly algorithm to dynamically adjust the nodes.Results The results showed that the average clustering accuracies in the Wine and Iris data sets were 86.59%and 94.55%,respectively,demonstrating good clustering performance.When calculating the node mortality rate and network load balancing standard deviation,the proposed algorithm showed dead nodes at approximately 50 iterations,with an average load balancing standard deviation of 1.7×10^(4),proving its contribution to extending the network lifespan.Conclusions This demonstrates the superiority of the proposed algorithm in significantly improving the energy efficiency and load balancing of wireless sensor networks to extend the network lifespan.The research results indicate that wireless sensor networks have theoretical and practical significance in fields such as monitoring,healthcare,and agriculture.
基金funded by the Northern Border University,Arar,KSA,under the project number“NBU-FFR-2025-3555-07”.
文摘Healthcare networks are transitioning from manual records to electronic health records,but this shift introduces vulnerabilities such as secure communication issues,privacy concerns,and the presence of malicious nodes.Existing machine and deep learning-based anomalies detection methods often rely on centralized training,leading to reduced accuracy and potential privacy breaches.Therefore,this study proposes a Blockchain-based-Federated Learning architecture for Malicious Node Detection(BFL-MND)model.It trains models locally within healthcare clusters,sharing only model updates instead of patient data,preserving privacy and improving accuracy.Cloud and edge computing enhance the model’s scalability,while blockchain ensures secure,tamper-proof access to health data.Using the PhysioNet dataset,the proposed model achieves an accuracy of 0.95,F1 score of 0.93,precision of 0.94,and recall of 0.96,outperforming baseline models like random forest(0.88),adaptive boosting(0.90),logistic regression(0.86),perceptron(0.83),and deep neural networks(0.92).
基金funded by the Researchers Supporting Project Number RSPD2024R681,King Saud University,Riyadh,Saudi Arabia.
文摘In a post-disaster environment characterized by frequent interruptions in communication links,traditional wireless communication networks are ineffective.Although the“store-carry-forward”mechanism characteristic of Delay Tolerant Networks(DTNs)can transmit data from Internet of things devices to more reliable base stations or data centres,it also suffers from inefficient data transmission and excessive transmission delays.To address these challenges,we propose an intelligent routing strategy based on node sociability for post-disaster emergency network scenarios.First,we introduce an intelligent routing strategy based on node intimacy,which selects more suitable relay nodes and assigns the corresponding number of message copies based on comprehensive utility values.Second,we present an intelligent routing strategy based on geographical location of nodes to forward message replicas secondarily based on transmission utility values.Finally,experiments demonstrate the effectiveness of our proposed algorithm in terms of message delivery rate,network cost ratio and average transmission delay.
基金supported by the National Natural Science Foundation of China (U1808205)Hebei Natural Science Foundation (F2000501005)。
文摘This paper studies the target controllability of multilayer complex networked systems,in which the nodes are highdimensional linear time invariant(LTI)dynamical systems,and the network topology is directed and weighted.The influence of inter-layer couplings on the target controllability of multi-layer networks is discussed.It is found that even if there exists a layer which is not target controllable,the entire multi-layer network can still be target controllable due to the inter-layer couplings.For the multi-layer networks with general structure,a necessary and sufficient condition for target controllability is given by establishing the relationship between uncontrollable subspace and output matrix.By the derived condition,it can be found that the system may be target controllable even if it is not state controllable.On this basis,two corollaries are derived,which clarify the relationship between target controllability,state controllability and output controllability.For the multi-layer networks where the inter-layer couplings are directed chains and directed stars,sufficient conditions for target controllability of networked systems are given,respectively.These conditions are easier to verify than the classic criterion.
基金the National Key Research and Development Program of China(No.2020YFB1005805)Peng Cheng Laboratory Project(Grant No.PCL2021A02)+2 种基金Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies(2022B1212010005)Shenzhen Basic Research(General Project)(No.JCYJ20190806142601687)Shenzhen Stable Supporting Program(General Project)(No.GXWD20201230155427003-20200821160539001).
文摘Ethereum, currently the most widely utilized smart contracts platform, anchors the security of myriad smartcontracts upon its own robustness. Its foundational peer-to-peer network facilitates a dependable node connectionmechanism, whereas an efficient data-sharing protocol constitutes as the bedrock of Blockchain network security.In this paper, we propose NodeHunter, an Ethereum network detector implemented through the application ofsimulation technology, which is capable of aggregating all node records within the network and the interconnectednessbetween them. Utilizing this connection information, NodeHunter can procure more comprehensive insightsfor network status analysis compared to preceding detection methodologies. Throughout a three-month period ofunbroken surveillance of the Ethereum network, we obtained an excess of two million node records along with overone hundred million node acquaintances. Analysis of the gathered data revealed that an alarming 49% or more ofthese node records were maliciously forged.
基金Scientific Research Project of Liaoning Province Education Department,Code:LJKQZ20222457&LJKMZ20220781Liaoning Province Nature Fund Project,Code:No.2022-MS-291.
文摘As industrialization and informatization becomemore deeply intertwined,industrial control networks have entered an era of intelligence.The connection between industrial control networks and the external internet is becoming increasingly close,which leads to frequent security accidents.This paper proposes a model for the industrial control network.It includes a malware containment strategy that integrates intrusion detection,quarantine,and monitoring.Basedonthismodel,the role of keynodes in the spreadofmalware is studied,a comparisonexperiment is conducted to validate the impact of the containment strategy.In addition,the dynamic behavior of the model is analyzed,the basic reproduction number is computed,and the disease-free and endemic equilibrium of the model is also obtained by the basic reproduction number.Moreover,through simulation experiments,the effectiveness of the containment strategy is validated,the influence of the relevant parameters is analyzed,and the containment strategy is optimized.In otherwords,selective immunity to key nodes can effectively suppress the spread ofmalware andmaintain the stability of industrial control systems.The earlier the immunization of key nodes,the better.Once the time exceeds the threshold,immunizing key nodes is almost ineffective.The analysis provides a better way to contain the malware in the industrial control network.
基金supported by the Fundamental Research Funds for the Central Universities,China.
文摘Aiming at the problem that it is difficult to build model and identify the vulnerable equipment for aviation armament System-of-Systems(SoS)due to complex equipment interaction relationships and high confrontation,the interdependent network theory is introduced to solve it.Firstly,a two-layer heterogeneous interdependent network model for aviation armament SoS is proposed,which reflects the information interaction,functional dependency and inter-network dependence effectively.Secondly,using the attack cost to describe the confrontation process and taking the comprehensive impact on kill chains as the entry point,the node importance index and the attack cost measurement method are constructed.Thirdly,the identification of vulnerable nodes is transformed into the optimization problem of node combinatorial selection,and the vulnerable node identification method based on tabu search is proposed.Based on vulnerable nodes,a robustness enhancement strategy for aviation armament SoS network is presented.Finally,the above methods are used to an aerial confrontation SoS,and the results verify the rationality and effectiveness of the proposed methods.
基金Project supported by the National Natural Science Foundation of China (Grant Nos.62373197 and 61873326)。
文摘In many engineering networks, only a part of target state variables are required to be estimated.On the other hand,multi-layer complex network exists widely in practical situations.In this paper, the state estimation of target state variables in multi-layer complex dynamical networks with nonlinear node dynamics is studied.A suitable functional state observer is constructed with the limited measurement.The parameters of the designed functional observer are obtained from the algebraic method and the stability of the functional observer is proven by the Lyapunov theorem.Some necessary conditions that need to be satisfied for the design of the functional state observer are obtained.Different from previous studies, in the multi-layer complex dynamical network with nonlinear node dynamics, the proposed method can estimate the state of target variables on some layers directly instead of estimating all the individual states.Thus, it can greatly reduce the placement of observers and computational cost.Numerical simulations with the three-layer complex dynamical network composed of three-dimensional nonlinear dynamical nodes are developed to verify the effectiveness of the method.
基金supported by the National Natural Science Foundation of China(61573077,U1808205)China Scholarship Council(202308130119)Natural Science Foundation of Hebei Province(F2022501005)。
文摘This paper studies the controllability of networked systems,in which the nodes are heterogeneous high-dimensional dynamical systems,and the links between nodes are multi-relational.Our aim is to find controllability criteria for heterogeneous networks with multi-relational links beyond those only applicable to networks with single-relational links.It is found a network with multi-relational links can be controllable even if each single-relational network topology is uncontrollable,and vice versa.Some sufficient and necessary conditions are derived for the controllability of multi-relational networks with heterogeneous dynamical nodes.For two typical multi-relational networks with star-chain topology and star-circle topology,some easily verified conditions are presented.For illustration and verification,several examples are presented.These findings provide practical insights for the analysis and control of multi-relational complex systems.
基金Project partially supported by the National Natural Science Foundation of China (Grant Nos. 61672298 and 62373197)the Major Project of Philosophy and Social Science Research in Colleges and Universities in Jiangsu Province,China (Grant No. 2018SJZDI142)the Postgraduate Research & Practice Innovation Program of Jiangsu Province,China (Grant No. KYCX23 1045)。
文摘Identifying key nodes in complex networks is crucial for understanding and controlling their dynamics. Traditional centrality measures often fall short in capturing the multifaceted roles of nodes within these networks. The Page Rank algorithm, widely recognized for ranking web pages, offers a more nuanced approach by considering the importance of connected nodes. However, existing methods generally overlook the geometric properties of networks, which can provide additional insights into their structure and functionality. In this paper, we propose a novel method named Curv-Page Rank(C-PR), which integrates network curvature and Page Rank to identify influential nodes in complex networks. By leveraging the geometric insights provided by curvature alongside structural properties, C-PR offers a more comprehensive measure of a node's influence. Our approach is particularly effective in networks with community structures, where it excels at pinpointing bridge nodes critical for maintaining connectivity and facilitating information flow. We validate the effectiveness of C-PR through extensive experiments. The results demonstrate that C-PR outperforms traditional centrality-based and Page Rank methods in identifying critical nodes. Our findings offer fresh insights into the structural importance of nodes across diverse network configurations, highlighting the potential of incorporating geometric properties into network analysis.
文摘In order to reveal the complex network characteristics and evolution principle of China aviation network,the relationship between the node degree and the average path length of China aviation network in 1988,1994,2001,2008 and 2015 was studied.According to the theory and method of complex network,the network system was constructed with the city where the airport was located as the network node and the airline as the edge of the network.On the basis of the statistical data,the node average path length of China aviation network in 1988,1994,2001,2008 and 2015 was calculated.Through regression analysis,it was found that the node degree had a logarithmic relationship with the average length of node path,and the two parameters of the logarithmic relationship had linear evolutionary trace.Key word:China aviation network,complex network,node degree,average length of node path,logarithmic relationship,evolutionary trace.
基金Shaanxi Science Fund for Distinguished Young Scholars,Grant/Award Number:2024JC-JCQN-57Xi’an Science and Technology Plan Project,Grant/Award Number:2023JH-QCYJQ-0086+2 种基金Scientific Research Program Funded by Education Department of Shaanxi Provincial Government,Grant/Award Number:P23JP071Engineering Technology Research Center of Shaanxi Province for Intelligent Testing and Reliability Evaluation of Electronic Equipments,Grant/Award Number:2023-ZC-GCZX-00472022 Shaanxi University Youth Innovation Team Project。
文摘The use of dynamic programming(DP)algorithms to learn Bayesian network structures is limited by their high space complexity and difficulty in learning the structure of large-scale networks.Therefore,this study proposes a DP algorithm based on node block sequence constraints.The proposed algorithm constrains the traversal process of the parent graph by using the M-sequence matrix to considerably reduce the time consumption and space complexity by pruning the traversal process of the order graph using the node block sequence.Experimental results show that compared with existing DP algorithms,the proposed algorithm can obtain learning results more efficiently with less than 1%loss of accuracy,and can be used for learning larger-scale networks.
基金Project supported by the National Natural Science Foundation of China(Grant No.62176217)the Program from the Sichuan Provincial Science and Technology,China(Grant No.2018RZ0081)the Fundamental Research Funds of China West Normal University(Grant No.17E063).
文摘Graph neural networks(GNNs)have demonstrated excellent performance in graph representation learning.However,as the volume of graph data grows,issues related to cost and efficiency become increasingly prominent.Graph distillation methods address this challenge by extracting a smaller,reduced graph,ensuring that GNNs trained on both the original and reduced graphs show similar performance.Existing methods,however,primarily optimize the feature matrix of the reduced graph and rely on correlation information from GNNs,while neglecting the original graph’s structure and redundant nodes.This often results in a loss of critical information within the reduced graph.To overcome this limitation,we propose a graph distillation method guided by network symmetry.Specifically,we identify symmetric nodes with equivalent neighborhood structures and merge them into“super nodes”,thereby simplifying the network structure,reducing redundant parameter optimization and enhancing training efficiency.At the same time,instead of relying on the original node features,we employ gradient descent to match optimal features that align with the original features,thus improving downstream task performance.Theoretically,our method guarantees that the reduced graph retains the key information present in the original graph.Extensive experiments demonstrate that our approach achieves significant improvements in graph distillation,exhibiting strong generalization capability and outperforming existing graph reduction methods.
基金supported by National Natural Science Foundation of China(No.61862038)Lanzhou Talent Innovation and Entrepreneurship Technology Plan Project(No.2019-RC-14).
文摘Aiming at node deployment in the monitoring area of the field observation instrument network in the cold and arid regions,we propose a virtual force algorithm based on Voronoi diagram(VFAVD),which adopts probabilistic sensing model that is more in line with the actual situation.First,the Voronoi diagram is constructed in the monitoring area to determine the Thiessen polygon of each node.Then,the virtual force on each node is calculated,and the node update its position according to the direction and size of the total force,so as to achieve the purpose of improving the network coverage rate.The simulation results show that the proposed algorithm can effectively improve the coverage rate of the network,and also has a good effect on the coverage uniformity.
基金Project supported by the National Natural Science Foundation of China (Grant No. 71971150)the Project of Research Center for System Sciences and Enterprise Development (Grant No. Xq16B05)the Fundamental Research Funds for the Central Universities of China (Grant No. SXYPY202313)。
文摘As a key mode of transportation, urban metro networks have significantly enhanced urban traffic environments and travel efficiency, making the identification of critical stations within these networks increasingly essential. This study presents a novel integrated topological-functional(ITF) algorithm for identifying critical nodes, combining topological metrics such as K-shell decomposition, node information entropy, and neighbor overlapping interaction with the functional attributes of passenger flow operations, while also considering the coupling effects between metro and bus networks. Using the Chengdu metro network as a case study, the effectiveness of the algorithm under different conditions is validated.The results indicate significant differences in passenger flow patterns between working and non-working days, leading to varying sets of critical nodes across these scenarios. Moreover, the ITF algorithm demonstrates a marked improvement in the accuracy of critical node identification compared to existing methods. This conclusion is supported by the analysis of changes in the overall network structure and relative global operational efficiency following targeted attacks on the identified critical nodes. The findings provide valuable insight into urban transportation planning, offering theoretical and practical guidance for improving metro network safety and resilience.
基金funded by Sardar Vallabhbhai National Institute of Technology through SEED grant No.Dean(R&C)/SEED Money/2021-22/11153Date:08/02/2022supported by Business Finland EWARE-6G project under 6G Bridge program,and in part by theHorizon Europe(Smart Networks and Services Joint Under taking)program under Grant Agreement No.101096838(6G-XR project).
文摘Wireless Sensor Networks(WSNs)play a critical role in automated border surveillance systems,where continuous monitoring is essential.However,limited energy resources in sensor nodes lead to frequent network failures and reduced coverage over time.To address this issue,this paper presents an innovative energy-efficient protocol based on deep Q-learning(DQN),specifically developed to prolong the operational lifespan of WSNs used in border surveillance.By harnessing the adaptive power of DQN,the proposed protocol dynamically adjusts node activity and communication patterns.This approach ensures optimal energy usage while maintaining high coverage,connectivity,and data accuracy.The proposed system is modeled with 100 sensor nodes deployed over a 1000 m×1000 m area,featuring a strategically positioned sink node.Our method outperforms traditional approaches,achieving significant enhancements in network lifetime and energy utilization.Through extensive simulations,it is observed that the network lifetime increases by 9.75%,throughput increases by 8.85%and average delay decreases by 9.45%in comparison to the similar recent protocols.It demonstrates the robustness and efficiency of our protocol in real-world scenarios,highlighting its potential to revolutionize border surveillance operations.
基金funded by the Key Research and Development Program of Zhejiang Province No.2023C01141the Science and Technology Innovation Community Project of the Yangtze River Delta No.23002410100suported by the Open Research Fund of the State Key Laboratory of Blockchain and Data Security,Zhejiang University.
文摘Graph Neural Networks(GNNs)have demonstrated outstanding capabilities in processing graph-structured data and are increasingly being integrated into large-scale pre-trained models,such as Large Language Models(LLMs),to enhance structural reasoning,knowledge retrieval,and memory management.The expansion of their application scope imposes higher requirements on the robustness of GNNs.However,as GNNs are applied to more dynamic and heterogeneous environments,they become increasingly vulnerable to real-world perturbations.In particular,graph data frequently encounters joint adversarial perturbations that simultaneously affect both structures and features,which are significantly more challenging than isolated attacks.These disruptions,caused by incomplete data,malicious attacks,or inherent noise,pose substantial threats to the stable and reliable performance of traditional GNN models.To address this issue,this study proposes the Dual-Shield Graph Neural Network(DSGNN),a defense model that simultaneously mitigates structural and feature perturbations.DSGNN utilizes two parallel GNN channels to independently process structural noise and feature noise,and introduces an adaptive fusion mechanism that integrates information from both pathways to generate robust node representations.Theoretical analysis demonstrates that DSGNN achieves a tighter robustness boundary under joint perturbations compared to conventional single-channel methods.Experimental evaluations across Cora,CiteSeer,and Industry datasets show that DSGNN achieves the highest average classification accuracy under various adversarial settings,reaching 81.24%,71.94%,and 81.66%,respectively,outperforming GNNGuard,GCN-Jaccard,GCN-SVD,RGCN,and NoisyGNN.These results underscore the importance of multi-view perturbation decoupling in constructing resilient GNN models for real-world applications.
基金National Natural Science Foundation of China (Grant No. 81800698)Jiangsu Provincial Medical Key Discipline Cultivation Unit (Grant No. JSDW202241)+1 种基金Research Project of Jiangsu Commission of Health (Grant No. H2023053)Zhenjiang Science and the Technology Planning Project (Grant Nos. SH2023006and SH2023008)。
文摘Most papillary thyroid carcinoma(PTC) patients have a good prognosis. However, lymph node metastasis(LNM), the most common manifestation of disease progression, is frequently associated with a poor prognosis.Nevertheless, few studies have focused on the underlying mechanisms of LNM. In the current study, we aimed to investigate the potential role of exosomal circRNAs that contribute to LNM in PTC. We identified 9 000 differentially expressed exosomal circRNAs in PTC patients with LNM, including 684 upregulated and 2 193 downregulated circRNAs. Functional enrichment analysis revealed that these differentially expressed circRNAs were primarily involved in a variety of molecular and signaling pathways correlated with PTC progression and LNM. Through bioinformatics analysis, we identified 14 circRNA-miRNA-mRNA networks related to LNM-associated signaling pathways in PTC. Moreover, both circTACC2-miR-7-EGFR and circBIRC6-miR-24-3p-BCL2L11 axes were verified for their potential involvement in PTC with LNM. Additionally, we identified four upregulated circRNA-related hub genes and eight hub genes correlated with downregulated circRNAs, some of which were validated as being potentially involved in LNM in PTC. Collectively, our findings provide a novel framework for an in-depth investigation of the function of dysregulated exosomal circRNAs and their potential as biomarkers in PTC patients with LNM.
基金the National Natural Science Foundation of China(Nos.11861045 and 62162040)。
文摘Essential proteins are an indispensable part of cells and play an extremely significant role in genetic disease diagnosis and drug development.Therefore,the prediction of essential proteins has received extensive attention from researchers.Many centrality methods and machine learning algorithms have been proposed to predict essential proteins.Nevertheless,the topological characteristics learned by the centrality method are not comprehensive enough,resulting in low accuracy.In addition,machine learning algorithms need sufficient prior knowledge to select features,and the ability to solve imbalanced classification problems needs to be further strengthened.These two factors greatly affect the performance of predicting essential proteins.In this paper,we propose a deep learning framework based on temporal convolutional networks to predict essential proteins by integrating gene expression data and protein-protein interaction(PPI)network.We make use of the method of network embedding to automatically learn more abundant features of proteins in the PPI network.For gene expression data,we treat it as sequence data,and use temporal convolutional networks to extract sequence features.Finally,the two types of features are integrated and put into the multi-layer neural network to complete the final classification task.The performance of our method is evaluated by comparing with seven centrality methods,six machine learning algorithms,and two deep learning models.The results of the experiment show that our method is more effective than the comparison methods for predicting essential proteins.
