The outbreak of COVID-19 in 2019 has made people pay more attention to infectious diseases.In order to reduce the risk of infection and prevent the spread of infectious diseases,it is crucial to strengthen individual ...The outbreak of COVID-19 in 2019 has made people pay more attention to infectious diseases.In order to reduce the risk of infection and prevent the spread of infectious diseases,it is crucial to strengthen individual immunization measures and to restrain the diffusion of negative information relevant to vaccines at the opportune moment.This study develops a three-layer coupling model within the framework of hypernetwork evolution,examining the interplay among negative information,immune behavior,and epidemic propagation.Firstly,the dynamic topology evolution process of hypernetwork includes node joining,aging out,hyperedge adding and reconnecting.The three-layer communication model accounts for the multifaceted influences exerted by official media channels,subjective psychological acceptance capabilities,self-identification abilities,and physical fitness levels.Each level of the decision-making process is described using the Heaviside step function.Secondly,the dynamics equations of each state and the prevalence threshold are derived using the microscopic Markov chain approach(MMCA).The results show that the epidemic threshold is affected by three transmission processes.Finally,through the simulation testing,it is possible to enhance the intensity of official clarification,improve individual self-identification ability and physical fitness,and thereby promote the overall physical enhancement of society.This,in turn,is beneficial in controlling false information,heightening vaccination coverage,and controlling the epidemic.展开更多
The concepts of hypernetwork, composite hypergraph and its primary subhyper-graph are introduced, and the principle and algorithm of a new topological method-pri-mary subhypergraph method is presented for linear activ...The concepts of hypernetwork, composite hypergraph and its primary subhyper-graph are introduced, and the principle and algorithm of a new topological method-pri-mary subhypergraph method is presented for linear active hypernetwork analysis. The ex-pressions of the symbolic network functions generated by this method are very compactand contain no cancellation terms. Its computing time complexity is O(m^3c^2n_h+m_1u_G∑n_l);its order of magnitude is less than that in Refs. [1,2] by 2-3 orders.展开更多
The Social Internet of Things(SIoT)integrates the Internet of Things(IoT)and social networks,taking into account the social attributes of objects and diversifying the relationship between humans and objects,which over...The Social Internet of Things(SIoT)integrates the Internet of Things(IoT)and social networks,taking into account the social attributes of objects and diversifying the relationship between humans and objects,which overcomes the limitations of the IoT’s focus on associations between objects.Artificial Intelligence(AI)technology is rapidly evolving.It is critical to build trustworthy and transparent systems,especially with system security issues coming to the surface.This paper emphasizes the social attributes of objects and uses hypergraphs to model the diverse entities and relationships in SIoT,aiming to build an SIoT hypergraph generation model to explore the complex interactions between entities in the context of intelligent SIoT.Current hypergraph generation models impose too many constraints and fail to capture more details of real hypernetworks.In contrast,this paper proposes a hypergraph generation model that evolves dynamically over time,where only the number of nodes is fixed.It combines node wandering with a forest fire model and uses two different methods to control the size of the hyperedges.As new nodes are added,the model can promptly reflect changes in entities and relationships within SIoT.Experimental results exhibit that our model can effectively replicate the topological structure of real-world hypernetworks.We also evaluate the vulnerability of the hypergraph under different attack strategies,which provides theoretical support for building a more robust intelligent SIoT hypergraph model and lays the foundation for building safer and more reliable systems in the future.展开更多
The concepts of the undirected and directed decompositions are introduced for a hyperedge.Then, the recursive formulas of the underected decomposition set SD(m) and directed decomposition set SPD(m) are derived for an...The concepts of the undirected and directed decompositions are introduced for a hyperedge.Then, the recursive formulas of the underected decomposition set SD(m) and directed decomposition set SPD(m) are derived for an m-vertex hyperedge.Furthermore,the recursive formulas of their cardinalities|SD(m)|and |SPD(m)| are yielded.展开更多
A new branch of hypergraph theory-directed hyperaph theory and a kind of new methods-dicomposition contraction(DCP, PDCP and GDC) methods are presented for solving hypernetwork problems.lts computing time is lower tha...A new branch of hypergraph theory-directed hyperaph theory and a kind of new methods-dicomposition contraction(DCP, PDCP and GDC) methods are presented for solving hypernetwork problems.lts computing time is lower than that of ECP method in several order of magnitude.展开更多
Complex hypernetworks are ubiquitous in the real system. It is very important to investigate the evolution mecha- nisms. In this paper, we present a local-world evolving hypernetwork model by taking into account the h...Complex hypernetworks are ubiquitous in the real system. It is very important to investigate the evolution mecha- nisms. In this paper, we present a local-world evolving hypernetwork model by taking into account the hyperedge growth and local-world hyperedge preferential attachment mechanisms. At each time step, a newly added hyperedge encircles a new coming node and a number of nodes from a randomly selected local world. The number of the selected nodes from the local world obeys the uniform distribution and its mean value is m. The analytical and simulation results show that the hyperdegree approximately obeys the power-law form and the exponent of hyperdegree distribution is 7 = 2 ＋ 1/m. Furthermore, we numerically investigate the node degree, hyperedge degree, clustering coefficient, as well as the average distance, and find that the hypemetwork model shares the scale-flee and small-world properties, which shed some light for deeply understanding the evolution mechanism of the real systems.展开更多
Recent years have seen growing demand for the use of edge computing to achieve the full potential of the Internet of Things(IoTs),given that various IoT systems have been generating big data to facilitate modern laten...Recent years have seen growing demand for the use of edge computing to achieve the full potential of the Internet of Things(IoTs),given that various IoT systems have been generating big data to facilitate modern latency-sensitive applications.Network Dismantling(ND),which is a basic problem,attempts to find an optimal set of nodes that will maximize the connectivity degradation in a network.However,current approaches mainly focus on simple networks that model only pairwise interactions between two nodes,whereas higher-order groupwise interactions among an arbitrary number of nodes are ubiquitous in the real world,which can be better modeled as hypernetwork.The structural difference between a simple and a hypernetwork restricts the direct application of simple ND methods to a hypernetwork.Although some hypernetwork centrality measures(e.g.,betweenness)can be used for hypernetwork dismantling,they face the problem of balancing effectiveness and efficiency.Therefore,we propose a betweenness approximation-based hypernetwork dismantling method with a Hypergraph Neural Network(HNN).The proposed approach,called“HND”,trains a transferable HNN-based regression model on plenty of generated small-scale synthetic hypernetworks in a supervised way,utilizing the well-trained model to approximate the betweenness of the nodes.Extensive experiments on five actual hypernetworks demonstrate the effectiveness and efficiency of HND compared with various baselines.展开更多
Deep learning enables real-time resource allocation for ultra-reliable and low-latency communications(URLLC),one of the major use cases in the next-generation cellular networks.Yet the high training complexity and wea...Deep learning enables real-time resource allocation for ultra-reliable and low-latency communications(URLLC),one of the major use cases in the next-generation cellular networks.Yet the high training complexity and weak generalization ability of neural networks impede the practical use of the learning-based methods in dynamic wireless environments.To overcome these obstacles,we propose a parameter generation network(PGN)to efficiently learn bandwidth and power allocation policies in URLLC.The PGN consists of two types of fully-connected neural networks(FNNs).One is a policy network,which is used to learn a resource allocation policy or a Lagrangian multiplier function.The other type of FNNs are hypernetworks,which are designed to learn the weight matrices and bias vectors of the policy network.Only the hypernetworks require training.Using the well-trained hypernetworks,the policy network is generated through forward propagation in the test phase.By introducing a simple data processing,the hypernetworks can well learn the weight matrices and bias vectors by inputting their indices,resulting in low training cost.Simulation results demonstrate that the learned bandwidth and power allocation policies by the PGNs perform very close to a numerical algorithm.Moreover,the PGNs can be well generalized to the number of users and wireless channels,and are with significantly lower memory costs,fewer training samples,and shorter training time than the traditional learning-based methods.展开更多
Psychiatric disorders exhibit extremely high heterogeneity,thus making accurate diagnosis and timely treatment challenging.Numerous neuroimaging studies have revealed abnormal changes in brain functional connectivity ...Psychiatric disorders exhibit extremely high heterogeneity,thus making accurate diagnosis and timely treatment challenging.Numerous neuroimaging studies have revealed abnormal changes in brain functional connectivity among patients with psychiatric disorders.To better understand the complexity of these disorders,researchers have explored hypergraph-based methods.Using functional magnetic resonance imaging data and hypergraph theory,studies have modeled and analyzed brain functional connectivity hypernetworks to classify psychiatric disorders and identify associated biomarkers.Furthermore,modeling a subjects-level hypergraph aids in estimating potential higher-order relationships among individuals;thus,hypergraphs can be used for classifying psychiatric disorders and identifying biomarkers.Recent neuroimaging studies have revealed specific subtypes of psychiatric disorders with biological importance.Hypergraph-based clustering methods have been used to investigate subtypes of psychiatric disorders.However,limited work has surveyed the applications of hypergraph-based methods in classifying and subtyping psychiatric disorders.To address this gap,this article provides a thorough survey,and discusses current challenges and potential future research directions in this field.展开更多
Many phenomena in realistic complex systems can be explained by the synchronisation behavior of complex systems,such as cricket chirping in uni-son.The synchronisation behavior occurring on a hypernetwork can be used ...Many phenomena in realistic complex systems can be explained by the synchronisation behavior of complex systems,such as cricket chirping in uni-son.The synchronisation behavior occurring on a hypernetwork can be used to explain the swarming behavior occurring on a multivariate interacting system,such as the synchronised forwarding of group messages.There is a lack of results related to phase synchronization of hypernetwork in the existing studies on the synchronization behavior of hypernetworks.To address this problem,this paper investigates the node-based and hyperedge-based phase synchronisation of a scale-free hypernetwork using the Kuramoto model with the order parameter r as the synchronisation degree indicator.The comparative analysis reveals that the phase synchronisation of the scale-free hypernetwork is related to the uniformity k of the hypernetwork but not to the number of nodes and hyperedges,and the phase synchronisation based on hyperedges is more likely to occur than that based on nodes as the coupling strength increases.In addition,the degree of phase syn-chronisation of scale-free hypernetworks is related to the number of new_nodes of newly added nodes when the hyperedge grows during the construction of the hypernetwork,which shows that the smaller the new_nodes is,the better the degree of synchronisation of the hypernetwork is.展开更多
文摘The outbreak of COVID-19 in 2019 has made people pay more attention to infectious diseases.In order to reduce the risk of infection and prevent the spread of infectious diseases,it is crucial to strengthen individual immunization measures and to restrain the diffusion of negative information relevant to vaccines at the opportune moment.This study develops a three-layer coupling model within the framework of hypernetwork evolution,examining the interplay among negative information,immune behavior,and epidemic propagation.Firstly,the dynamic topology evolution process of hypernetwork includes node joining,aging out,hyperedge adding and reconnecting.The three-layer communication model accounts for the multifaceted influences exerted by official media channels,subjective psychological acceptance capabilities,self-identification abilities,and physical fitness levels.Each level of the decision-making process is described using the Heaviside step function.Secondly,the dynamics equations of each state and the prevalence threshold are derived using the microscopic Markov chain approach(MMCA).The results show that the epidemic threshold is affected by three transmission processes.Finally,through the simulation testing,it is possible to enhance the intensity of official clarification,improve individual self-identification ability and physical fitness,and thereby promote the overall physical enhancement of society.This,in turn,is beneficial in controlling false information,heightening vaccination coverage,and controlling the epidemic.
文摘The concepts of hypernetwork, composite hypergraph and its primary subhyper-graph are introduced, and the principle and algorithm of a new topological method-pri-mary subhypergraph method is presented for linear active hypernetwork analysis. The ex-pressions of the symbolic network functions generated by this method are very compactand contain no cancellation terms. Its computing time complexity is O(m^3c^2n_h+m_1u_G∑n_l);its order of magnitude is less than that in Refs. [1,2] by 2-3 orders.
文摘The Social Internet of Things(SIoT)integrates the Internet of Things(IoT)and social networks,taking into account the social attributes of objects and diversifying the relationship between humans and objects,which overcomes the limitations of the IoT’s focus on associations between objects.Artificial Intelligence(AI)technology is rapidly evolving.It is critical to build trustworthy and transparent systems,especially with system security issues coming to the surface.This paper emphasizes the social attributes of objects and uses hypergraphs to model the diverse entities and relationships in SIoT,aiming to build an SIoT hypergraph generation model to explore the complex interactions between entities in the context of intelligent SIoT.Current hypergraph generation models impose too many constraints and fail to capture more details of real hypernetworks.In contrast,this paper proposes a hypergraph generation model that evolves dynamically over time,where only the number of nodes is fixed.It combines node wandering with a forest fire model and uses two different methods to control the size of the hyperedges.As new nodes are added,the model can promptly reflect changes in entities and relationships within SIoT.Experimental results exhibit that our model can effectively replicate the topological structure of real-world hypernetworks.We also evaluate the vulnerability of the hypergraph under different attack strategies,which provides theoretical support for building a more robust intelligent SIoT hypergraph model and lays the foundation for building safer and more reliable systems in the future.
文摘The concepts of the undirected and directed decompositions are introduced for a hyperedge.Then, the recursive formulas of the underected decomposition set SD(m) and directed decomposition set SPD(m) are derived for an m-vertex hyperedge.Furthermore,the recursive formulas of their cardinalities|SD(m)|and |SPD(m)| are yielded.
文摘A new branch of hypergraph theory-directed hyperaph theory and a kind of new methods-dicomposition contraction(DCP, PDCP and GDC) methods are presented for solving hypernetwork problems.lts computing time is lower than that of ECP method in several order of magnitude.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.71071098,91024026,and 71171136)supported by the Shanghai Rising-Star Program,China(Grant No.11QA1404500)the Leading Academic Discipline Project of Shanghai City,China(Grant No.XTKX2012)
文摘Complex hypernetworks are ubiquitous in the real system. It is very important to investigate the evolution mecha- nisms. In this paper, we present a local-world evolving hypernetwork model by taking into account the hyperedge growth and local-world hyperedge preferential attachment mechanisms. At each time step, a newly added hyperedge encircles a new coming node and a number of nodes from a randomly selected local world. The number of the selected nodes from the local world obeys the uniform distribution and its mean value is m. The analytical and simulation results show that the hyperdegree approximately obeys the power-law form and the exponent of hyperdegree distribution is 7 = 2 ＋ 1/m. Furthermore, we numerically investigate the node degree, hyperedge degree, clustering coefficient, as well as the average distance, and find that the hypemetwork model shares the scale-flee and small-world properties, which shed some light for deeply understanding the evolution mechanism of the real systems.
基金supported by the Anhui Province University Collaborative Innovation Project(No.GXXT-2022-091)the National Natural Science Foundation of China(No.62006003)+2 种基金the Natural Science Foundation of Anhui Province(No.2208085QF197)the Key Project of Nature Science Research for Universities of Anhui Province of China(Nos.2022AH040019 and 2022AH05008637)the Hefei Key Common Technology Project(No.GJ2022GX15).
文摘Recent years have seen growing demand for the use of edge computing to achieve the full potential of the Internet of Things(IoTs),given that various IoT systems have been generating big data to facilitate modern latency-sensitive applications.Network Dismantling(ND),which is a basic problem,attempts to find an optimal set of nodes that will maximize the connectivity degradation in a network.However,current approaches mainly focus on simple networks that model only pairwise interactions between two nodes,whereas higher-order groupwise interactions among an arbitrary number of nodes are ubiquitous in the real world,which can be better modeled as hypernetwork.The structural difference between a simple and a hypernetwork restricts the direct application of simple ND methods to a hypernetwork.Although some hypernetwork centrality measures(e.g.,betweenness)can be used for hypernetwork dismantling,they face the problem of balancing effectiveness and efficiency.Therefore,we propose a betweenness approximation-based hypernetwork dismantling method with a Hypergraph Neural Network(HNN).The proposed approach,called“HND”,trains a transferable HNN-based regression model on plenty of generated small-scale synthetic hypernetworks in a supervised way,utilizing the well-trained model to approximate the betweenness of the nodes.Extensive experiments on five actual hypernetworks demonstrate the effectiveness and efficiency of HND compared with various baselines.
基金supported by the Key Project of National Natural Science Foundation of China(NSFC)under Grant 61731002.
文摘Deep learning enables real-time resource allocation for ultra-reliable and low-latency communications(URLLC),one of the major use cases in the next-generation cellular networks.Yet the high training complexity and weak generalization ability of neural networks impede the practical use of the learning-based methods in dynamic wireless environments.To overcome these obstacles,we propose a parameter generation network(PGN)to efficiently learn bandwidth and power allocation policies in URLLC.The PGN consists of two types of fully-connected neural networks(FNNs).One is a policy network,which is used to learn a resource allocation policy or a Lagrangian multiplier function.The other type of FNNs are hypernetworks,which are designed to learn the weight matrices and bias vectors of the policy network.Only the hypernetworks require training.Using the well-trained hypernetworks,the policy network is generated through forward propagation in the test phase.By introducing a simple data processing,the hypernetworks can well learn the weight matrices and bias vectors by inputting their indices,resulting in low training cost.Simulation results demonstrate that the learned bandwidth and power allocation policies by the PGNs perform very close to a numerical algorithm.Moreover,the PGNs can be well generalized to the number of users and wireless channels,and are with significantly lower memory costs,fewer training samples,and shorter training time than the traditional learning-based methods.
基金supported by the National Natural Science Foundation of China(grant No.62076157 and 61703253 to YHD)the Fund Program for the Scientific Activities of Selected Returned Overseas Professionals in Shanxi Province(to YHD)the 1331 Engineering Project of Shanxi Province of China.
文摘Psychiatric disorders exhibit extremely high heterogeneity,thus making accurate diagnosis and timely treatment challenging.Numerous neuroimaging studies have revealed abnormal changes in brain functional connectivity among patients with psychiatric disorders.To better understand the complexity of these disorders,researchers have explored hypergraph-based methods.Using functional magnetic resonance imaging data and hypergraph theory,studies have modeled and analyzed brain functional connectivity hypernetworks to classify psychiatric disorders and identify associated biomarkers.Furthermore,modeling a subjects-level hypergraph aids in estimating potential higher-order relationships among individuals;thus,hypergraphs can be used for classifying psychiatric disorders and identifying biomarkers.Recent neuroimaging studies have revealed specific subtypes of psychiatric disorders with biological importance.Hypergraph-based clustering methods have been used to investigate subtypes of psychiatric disorders.However,limited work has surveyed the applications of hypergraph-based methods in classifying and subtyping psychiatric disorders.To address this gap,this article provides a thorough survey,and discusses current challenges and potential future research directions in this field.
文摘Many phenomena in realistic complex systems can be explained by the synchronisation behavior of complex systems,such as cricket chirping in uni-son.The synchronisation behavior occurring on a hypernetwork can be used to explain the swarming behavior occurring on a multivariate interacting system,such as the synchronised forwarding of group messages.There is a lack of results related to phase synchronization of hypernetwork in the existing studies on the synchronization behavior of hypernetworks.To address this problem,this paper investigates the node-based and hyperedge-based phase synchronisation of a scale-free hypernetwork using the Kuramoto model with the order parameter r as the synchronisation degree indicator.The comparative analysis reveals that the phase synchronisation of the scale-free hypernetwork is related to the uniformity k of the hypernetwork but not to the number of nodes and hyperedges,and the phase synchronisation based on hyperedges is more likely to occur than that based on nodes as the coupling strength increases.In addition,the degree of phase syn-chronisation of scale-free hypernetworks is related to the number of new_nodes of newly added nodes when the hyperedge grows during the construction of the hypernetwork,which shows that the smaller the new_nodes is,the better the degree of synchronisation of the hypernetwork is.
