During public health emergencies,the diffusion of negative information can exacerbate the transmission of adverse emotions,such as fear and anxiety.These emotions can adversely affect immune function and,consequently,...During public health emergencies,the diffusion of negative information can exacerbate the transmission of adverse emotions,such as fear and anxiety.These emotions can adversely affect immune function and,consequently,influence the spread of the epidemic.In this study,we established a coupled model incorporating environmental factors to explore the coevolution dynamic process of information-emotions-epidemic dynamics in activity-driven multiplex networks.In this model,environmental factors refer to the external conditions or pressures that affect the spread of information,emotions,and epidemics.These factors include media coverage,public opinion,and the prevalence of diseases in the neighborhood.These layers are dynamically cross-coupled,where the environmental factors in the information layer are influenced by the emotional layer;the higher the levels of anxious states among neighboring individuals,the greater the likelihood of information diffusion.Although environmental factors in the emotional layer are influenced by both the information and epidemic layers,they come from the factors of global information and the proportion of local infections among surrounding neighbors.Subsequently,we utilized the microscopic Markov chain approach to describe the dynamic processes,thereby obtaining the epidemic threshold.Finally,conclusions are drawn through numerical modeling and analysis.The conclusions suggest that when negative information increases,the probability of the transmission of anxious states across the population increases.The transmission of anxious states increases the final size of the disease and decreases its outbreak threshold.Reducing the impact of environmental factors at both the informational and emotional levels is beneficial for controlling the scale of the spread of the epidemic.Our findings can provide a reference for improving public health awareness and behavioral decision-making,mitigating the adverse impacts of anxious states,and ultimately controlling the spread of epidemics.展开更多
In modern wireless communication and electromagnetic control,automatic modulationclassification(AMC)of orthogonal frequency division multiplexing(OFDM)signals plays animportant role.However,under Doppler frequency shi...In modern wireless communication and electromagnetic control,automatic modulationclassification(AMC)of orthogonal frequency division multiplexing(OFDM)signals plays animportant role.However,under Doppler frequency shift and complex multipath channel conditions,extracting discriminative features from high-order modulation signals and ensuring model inter-pretability remain challenging.To address these issues,this paper proposes a Fourier attention net-work(FAttNet),which combines an attention mechanism with a Fourier analysis network(FAN).Specifically,the method directly converts the input signal to the frequency domain using the FAN,thereby obtaining frequency features that reflect the periodic variations in amplitude and phase.Abuilt-in attention mechanism then automatically calculates the weights for each frequency band,focusing on the most discriminative components.This approach improves both classification accu-racy and model interpretability.Experimental validation was conducted via high-order modulationsimulation using an RF testbed.The results show that under three different Doppler frequencyshifts and complex multipath channel conditions,with a signal-to-noise ratio of 10 dB,the classifi-cation accuracy can reach 89.1%,90.4%and 90%,all of which are superior to the current main-stream methods.The proposed approach offers practical value for dynamic spectrum access and sig-nal security detection,and it makes important theoretical contributions to the application of deeplearning in complex electromagnetic signal recognition.展开更多
The coronavirus disease 2019(COVID-19)has been widely spread around the world,and the control and behavior dynamics are still one of the important research directions in the world.Based on the characteristics of COVID...The coronavirus disease 2019(COVID-19)has been widely spread around the world,and the control and behavior dynamics are still one of the important research directions in the world.Based on the characteristics of COVID-19’s spread,a coupled disease-awareness model on multiplex networks is proposed in this paper to study and simulate the interaction between the spreading behavior of COVID-19 and related information.In the layer of epidemic spreading,the nodes can be divided into five categories,where the topology of the network represents the physical contact relationship of the population.The topological structure of the upper network shows the information interaction among the nodes,which can be divided into aware and unaware states.Awareness will make people play a positive role in preventing the epidemic diffusion,influencing the spread of the disease.Based on the above model,we have established the state transition equation,through the microscopic Markov chain approach(MMCA),and proposed the propagation threshold calculation method under the epidemic model.Furthermore,MMCA iteration and the Monte Carlo method are simulated on the static network and dynamic network,respectively.The current results will be beneficial to the study of COVID-19,and propose a more rational and effective model for future research on epidemics.展开更多
In recent years, the impact of information diffusion and individual behavior adoption patterns on epidemic transmission in complex networks has received significant attention. In the immunization behavior adoption pro...In recent years, the impact of information diffusion and individual behavior adoption patterns on epidemic transmission in complex networks has received significant attention. In the immunization behavior adoption process, different individuals often make behavioral decisions in different ways, and it is of good practical importance to study the influence of individual heterogeneity on the behavior adoption process. In this paper, we propose a three-layer coupled model to analyze the process of co-evolution of official information diffusion, immunization behavior adoption and epidemic transmission in multiplex networks, focusing on individual heterogeneity in behavior adoption patterns. Specifically, we investigate the impact of the credibility of social media and the risk sensitivity of the population on behavior adoption in further study of the effect of heterogeneity of behavior adoption on epidemic transmission. Then we use the microscopic Markov chain approach to describe the dynamic process and capture the evolution of the epidemic threshold. Finally, we conduct extensive simulations to prove our findings. Our results suggest that enhancing the credibility of social media can raise the epidemic transmission threshold, making it effective at controlling epidemic transmission during the dynamic process. In addition, improving an individuals' risk sensitivity, and thus their taking effective protective measures, can also reduce the number of infected individuals and delay the epidemic outbreak. Our study explores the role of individual heterogeneity in behavior adoption in real networks, more clearly models the effect of the credibility of social media and risk sensitivity of the population on the epidemic transmission dynamic, and provides a useful reference for managers to formulate epidemic control and prevention policies.展开更多
Over the last few years,the interplay between contagion dynamics of social influences(e.g.,human awareness,risk perception,and information dissemination)and biological infections has been extensively investigated with...Over the last few years,the interplay between contagion dynamics of social influences(e.g.,human awareness,risk perception,and information dissemination)and biological infections has been extensively investigated within the framework of multiplex networks.The vast majority of existing multiplex network spreading models typically resort to heterogeneous mean-field approximation and microscopic Markov chain approaches.Such approaches usually manifest richer dynamical properties on multiplex networks than those on simplex networks;however,they fall short of a subtle analysis of the variations in connections between nodes of the network and fail to account for the adaptive behavioral changes among individuals in response to epidemic outbreaks.To transcend these limitations,in this paper we develop a highly integrated effective degree approach to modeling epidemic and awareness spreading processes on multiplex networks coupled with awareness-dependent adaptive rewiring.This approach keeps track of the number of nearest neighbors in each state of an individual;consequently,it allows for the integration of changes in local contacts into the multiplex network model.We derive a formula for the threshold condition of contagion outbreak.Also,we provide a lower bound for the threshold parameter to indicate the effect of adaptive rewiring.The threshold analysis is confirmed by extensive simulations.Our results show that awareness-dependent link rewiring plays an important role in enhancing the transmission threshold as well as lowering the epidemic prevalence.Moreover,it is revealed that intensified awareness diffusion in conjunction with enhanced link rewiring makes a greater contribution to disease prevention and control.In addition,the critical phenomenon is observed in the dependence of the epidemic threshold on the awareness diffusion rate,supporting the metacritical point previously reported in literature.This work may shed light on understanding of the interplay between epidemic dynamics and social contagion on adaptive networks.展开更多
Spatial division multiplexing enabled elastic optical networks(SDM-EONs) are the potential implementation form of future optical transport networks, because it can curve the physical limitation of achievable transmiss...Spatial division multiplexing enabled elastic optical networks(SDM-EONs) are the potential implementation form of future optical transport networks, because it can curve the physical limitation of achievable transmission capacity in single-mode fiber and single-core fiber. However, spectrum fragmentation issue becomes more serious in SDM-EONs compared with simple elastic optical networks(EONs) with single mode fiber or single core fiber. In this paper, multicore virtual concatenation(MCVC) scheme is first proposed considering inter-core crosstalk to solve the spectrum fragmentation issue in SDM-EONs. Simulation results show that the proposed MCVC scheme can achieve better performance compared with the baseline scheme, i.e., single-core virtual concatenation(SCVC) scheme, in terms of blocking probability and spectrum utilization.展开更多
The problem of influence maximizing in social networks refers to obtaining a set of nodes of a specified size under a specific propagation model so that the aggregation of the node-set in the network has the greatest ...The problem of influence maximizing in social networks refers to obtaining a set of nodes of a specified size under a specific propagation model so that the aggregation of the node-set in the network has the greatest influence.Up to now,most of the research has tended to focus on monolayer network rather than on multiplex networks.But in the real world,most individuals usually exist in multiplex networks.Multiplex networks are substantially different as compared with those of a monolayer network.In this paper,we integrate the multi-relationship of agents in multiplex networks by considering the existing and relevant correlations in each layer of relationships and study the problem of unbalanced distribution between various relationships.Meanwhile,we measure the distribution across the network by the similarity of the links in the different relationship layers and establish a unified propagation model.After that,place on the established multiplex network propagation model,we propose a basic greedy algorithm on it.To reduce complexity,we combine some of the characteristics of triggering model into our algorithm.Then we propose a novel MNStaticGreedy algorithm which is based on the efficiency and scalability of the StaticGreedy algorithm.Our experiments show that the novel model and algorithm are effective,efficient and adaptable.展开更多
For networking of big data applications,an essential issue is how to represent networks in vector space for further mining and analysis tasks,e.g.,node classification,clustering,link prediction,and visualization.Most ...For networking of big data applications,an essential issue is how to represent networks in vector space for further mining and analysis tasks,e.g.,node classification,clustering,link prediction,and visualization.Most existing studies on this subject mainly concentrate on monoplex networks considering a single type of relation among nodes.However,numerous real-world networks are naturally composed of multiple layers with different relation types;such a network is called a multiplex network.The majority of existing multiplex network embedding methods either overlook node attributes,resort to node labels for training,or underutilize underlying information shared across multiple layers.In this paper,we propose Multiplex Network Infomax(MNI),an unsupervised embedding framework to represent information of multiple layers into a unified embedding space.To be more specific,we aim to maximize the mutual information between the unified embedding and node embeddings of each layer.On the basis of this framework,we present an unsupervised network embedding method for attributed multiplex networks.Experimental results show that our method achieves competitive performance on not only node-related tasks,such as node classification,clustering,and similarity search,but also a typical edge-related task,i.e.,link prediction,at times even outperforming relevant supervised methods,despite that MNI is fully unsupervised.展开更多
Disease is a serious threat to human society.Understanding the characteristics of disease transmission is helpful for people to effectively control disease.In real life,it is natural to take various measures when peop...Disease is a serious threat to human society.Understanding the characteristics of disease transmission is helpful for people to effectively control disease.In real life,it is natural to take various measures when people are aware of disease.In this paper,a novel coupled model considering asymmetric activity is proposed to describe the interactions between information diffusion and disease transmission in multiplex networks.Then,the critical threshold for disease transmission is derived by using the micro-Markov chain method.Finally,the theoretical results are verified by numerical simulations.The results show that reducing the activity level of individuals in the physical contact layer will have a continuous impact on reducing the disease outbreak threshold and suppressing the disease.In addition,the activity level of individuals in the virtual network has little impact on the transmission of the disease.Meanwhile,when individuals are aware of more disease-related information,the higher their awareness of prevention will be,which can effectively inhibit the transmission of disease.Our research results can provide a useful reference for the control of disease transmission.展开更多
Contrastive self‐supervised representation learning on attributed graph networks with Graph Neural Networks has attracted considerable research interest recently.However,there are still two challenges.First,most of t...Contrastive self‐supervised representation learning on attributed graph networks with Graph Neural Networks has attracted considerable research interest recently.However,there are still two challenges.First,most of the real‐word system are multiple relations,where entities are linked by different types of relations,and each relation is a view of the graph network.Second,the rich multi‐scale information(structure‐level and feature‐level)of the graph network can be seen as self‐supervised signals,which are not fully exploited.A novel contrastive self‐supervised representation learning framework on attributed multiplex graph networks with multi‐scale(named CoLM^(2)S)information is presented in this study.It mainly contains two components:intra‐relation contrast learning and interrelation contrastive learning.Specifically,the contrastive self‐supervised representation learning framework on attributed single‐layer graph networks with multi‐scale information(CoLMS)framework with the graph convolutional network as encoder to capture the intra‐relation information with multi‐scale structure‐level and feature‐level selfsupervised signals is introduced first.The structure‐level information includes the edge structure and sub‐graph structure,and the feature‐level information represents the output of different graph convolutional layer.Second,according to the consensus assumption among inter‐relations,the CoLM^(2)S framework is proposed to jointly learn various graph relations in attributed multiplex graph network to achieve global consensus node embedding.The proposed method can fully distil the graph information.Extensive experiments on unsupervised node clustering and graph visualisation tasks demonstrate the effectiveness of our methods,and it outperforms existing competitive baselines.展开更多
This paper presents a novel spec- trum sharing design aiming at optimising the performance of a Multiuser Orthogonal Freq- uency-Division Multiplexing （MU-OFDM） Co- gnitive Radio Network （CRN） that consists of mul...This paper presents a novel spec- trum sharing design aiming at optimising the performance of a Multiuser Orthogonal Freq- uency-Division Multiplexing （MU-OFDM） Co- gnitive Radio Network （CRN） that consists of multiple secondary Transmitter-Receiver （Tx-Rx） pairs. For most MU-OFDM systems, the Exc- lusive Subchannel Assignment （ESA） is an efficient resource allocation method. Noneth- eless, it is inappropriate for the network consi- dered in this paper, because subchannels shar- ing among secondary Tx-Rx pairs can further improve the system performance. We investi- gate the Weighted Sum Rate （WSR） maximi- zation problem under the Shared Subchannel Assignment （SSA）, where each subchannel is shared by multiple secondary Tx-Rx pairs. With Lagrangian duality technique, we decompose the original resource allocation problem into sev- eral sub-problems on each subchannel and pro- pose a duality-based suhchannel sharing ap- proach. For practical realisation in the cogni- tive systems without central control entity, a distributed duality-based WSR maximization scheme is presented. Simulation results mani- fest that the proposed scheme achieves sig- nificantly better performance than ESA duality scheme.展开更多
As the main food source for humans, the global movement of the three major grains significantly impacts human survival and development. To investigate the evolution of the world cereal trade network and its developmen...As the main food source for humans, the global movement of the three major grains significantly impacts human survival and development. To investigate the evolution of the world cereal trade network and its development trend, a weighted directed dynamic multiplexed network was established using historical data on cereal trade, cereal import dependency ratio, and arable land per capita. Inspired by the MLP framework, we redefined the weight determination method for computing layer weights and edge weights of the target layer, modified the CN, RA, AA, and PA indicators, and proposed the node similarity indicator for weighted directed networks. The AUC metric, which measures the accuracy of the algorithm, has also been improved in order to finally obtain the link prediction results for the grain trading network. The prediction results were processed, such as web-based presentation and community partition. It was found that the number of generalized trade agreements does not have a decisive impact on inter-country cereal trade. The former large grain exporters continue to play an important role in this trade network. In the future, the world trade in cereals will develop in the direction of more frequent intercontinental trade and gradually weaken the intracontinental cereal trade.展开更多
On-chip diffractive optical neural networks(DONNs)bring the advantages of parallel processing and low energy consumption.However,an accurate representation of the optical field’s evolution in the structure cannot be ...On-chip diffractive optical neural networks(DONNs)bring the advantages of parallel processing and low energy consumption.However,an accurate representation of the optical field’s evolution in the structure cannot be provided using the previous diffraction-based analysis method.Moreover,the loss caused by the open boundaries poses challenges to applications.A multimode DONN architecture based on a more precise eigenmode analysis method is proposed.We have constructed a universal library of input,output,and metaline structures utilizing this method,and realized a multimode DONN composed of the structures from the library.On the designed multimode DONNs with only one layer of the metaline,the classification task of an Iris plants dataset is verified with an accuracy of 90%on the blind test dataset,and the performance of the one-bit binary adder task is also validated.Compared to the previous architectures,the multimode DONN exhibits a more compact design and higher energy efficiency.展开更多
Future high-speed mobile communication systems require low latency and high capacity networks.Coherent wavelength division multiplexing(WDM)passive optical network(PON)scheme is expected to play a vital role in these ...Future high-speed mobile communication systems require low latency and high capacity networks.Coherent wavelength division multiplexing(WDM)passive optical network(PON)scheme is expected to play a vital role in these systems.In this paper,coherent WDM-PON scheme based on dual-polarization 16-quadrature amplitude modulation(DP-16 QAM)transceiver has been investigated.The aim of this scheme is to build a 2 Tbit/s(125 Gbit/s/λ×16 wavelengths)network that will be used in the construction of the transport architecture of fifth generation(5 G)and beyond 5 G(B5 G)cellular networks either in mobile front haul(MFH)or mobile back haul(MBH).The results indicate that the proposed scheme is very adequate for both 5 G and B5 G cellular networks requirements.展开更多
As a promising solution, virtualization is vigorously developed to eliminate the ossification of traditional Internet infrastructure and enhance the flexibility in sharing the substrate network (SN) resources includin...As a promising solution, virtualization is vigorously developed to eliminate the ossification of traditional Internet infrastructure and enhance the flexibility in sharing the substrate network (SN) resources including computing, storage, bandwidth, etc. With network virtualization, cloud service providers can utilize the shared substrate resources to provision virtual networks (VNs) and facilitate a wide and diverse range of applications. As more and more internet applications migrate to the cloud, the resource efficiency and the survivability of VNs, such as single link failure or large-scale disaster survivability, have become crucial issues. Elastic optical networks have emerged in recent years as a strategy for dealing with the divergence of network application bandwidth needs. The network capacity has been constrained due to the usage of only two multiplexing dimensions. As transmission rates rise, so does the demand for network failure protection. Due to their end-to-end solutions, those safe-guarding paths are of particular importance among the protection methods. Due to their end-to-end solutions, those safeguarding paths are of particular importance among the protection methods. This paper presents approaches that provide a failure-independent route-protecting p-cycle for path protection in space-division multiplexed elastic optical networks. This letter looks at two SDM network challenges and presents a heuristic technique (k-shortest path) for each. In the first approach, we study a virtual network embedding (SVNE) problem and propose an algorithm for EONs, which can combat against single-link failures. We evaluate the proposed POPETA algorithm and compare its performance with some counterpart algorithms. Simulation results demonstrate that the proposed algorithm can achieve satisfactory performance in terms of spectrum utilization and blocking ratio, even if with a higher backup redundancy ratio.展开更多
With the rise of cloud computing in recent years, a large number of streaming media has yielded an exponential growth in network traffic. With the now present 5G and future 6G, the development of the Internet of Thing...With the rise of cloud computing in recent years, a large number of streaming media has yielded an exponential growth in network traffic. With the now present 5G and future 6G, the development of the Internet of Things (IoT), social networks, video on demand, and mobile multimedia platforms, the backbone network is bound to bear more traffic. The transmission capacity of Single Core Fiber (SCFs) may be limited in the future and Spatial Division Multiplexing (SDM) leveraging multi-core fibers promises to be one of the solutions for the future. Currently, Elastic optical networks (EONs) with multi-core fibers (MCFs) are a kind of SDM-enabled EONs (SDM-EON) used to enhance the capacity of transmission. The resource assignment in MCFs, however, will be subject to Inter-Core Crosstalk (IC-XT), hence, reducing the effectiveness of transmission. This research highlights the routing, modulation level, and spectrum assignment (RMLSA) problems with anycast traffic mode in SDM-EON. A multipath routing scheme is used to reduce the blocking rate of anycast traffic in SDM-EON with the limit of inter-core crosstalk. Hence, an integer linear programming (ILP) problem is formulated and a heuristic algorithm is proposed. Two core-assignment strategies: First-Fit (FF) and Random-Fit (RF) are used and their performance is evaluated through simulations. The simulation results show that the multipath routing method is better than the single-path routing method in terms of blocking ratio and spectrum utilization ratio. Moreover, the FF is better than the RF in low traffic load in terms of blocking ratio (BR), and the opposite in high traffic load. The FF is better than the RF in terms of a spectrum utilization ratio. In an anycast protection problem, the proposed algorithm has a lower BR than previous works.展开更多
A passive optical network （PON） scheme based on optical code division multiplexing （OCDM） for the downstream traffics is proposed and analyzed in detail. In the PON, the downstream traffics are broadcasted by OCDM...A passive optical network （PON） scheme based on optical code division multiplexing （OCDM） for the downstream traffics is proposed and analyzed in detail. In the PON, the downstream traffics are broadcasted by OCDM technology to guarantee the security, while the upstream traffics pass through the same optical fiber by the common time division multiple access （TDMA） technology to decrease the cost. This schemes are denoted as OCDM/TDMA-PON, which can be applied to an optical access network （OAN） with full services on demand, such as Internet protocol, video on demand, tele-presence and high quality audio. The proposed OCDM/TDMA-PON scheme combines advantages of PON, TDMA, and OCDM technology. Simulation results indicate that the designed scheme improves the OAN performance, and enhances flexibility and scalability of the system.展开更多
In complex network of real world, there are many types of relationships between individuals, and the more effective research ways for this kind of network is to abstract these relationship as a multiplex network. More...In complex network of real world, there are many types of relationships between individuals, and the more effective research ways for this kind of network is to abstract these relationship as a multiplex network. More and more researchers are attracted to be engaged in multiplex network research. A novel framework of community detection of multiplex network based on consensus matrix was presented. Firstly, this framework merges the structure of multiplex network and the information of link between each node into monoplex network. Then, the community structure information of each layer network was obtained through consensus matrix, and the traditional community division algorithm was utilized to carry out community detection of combine networks. The experimental results show that the proposed algorithm can get better performance of community partition in the real network datasets.展开更多
基金partially supported by the National Natural Science Foundation of China(Grant No.72174121)the Program for Professor of Special Appointment(Eastern Scholar)at Shanghai Institutions of Higher Learningthe Natural Science Foundation of Shanghai(Grant No.21ZR1444100)。
文摘During public health emergencies,the diffusion of negative information can exacerbate the transmission of adverse emotions,such as fear and anxiety.These emotions can adversely affect immune function and,consequently,influence the spread of the epidemic.In this study,we established a coupled model incorporating environmental factors to explore the coevolution dynamic process of information-emotions-epidemic dynamics in activity-driven multiplex networks.In this model,environmental factors refer to the external conditions or pressures that affect the spread of information,emotions,and epidemics.These factors include media coverage,public opinion,and the prevalence of diseases in the neighborhood.These layers are dynamically cross-coupled,where the environmental factors in the information layer are influenced by the emotional layer;the higher the levels of anxious states among neighboring individuals,the greater the likelihood of information diffusion.Although environmental factors in the emotional layer are influenced by both the information and epidemic layers,they come from the factors of global information and the proportion of local infections among surrounding neighbors.Subsequently,we utilized the microscopic Markov chain approach to describe the dynamic processes,thereby obtaining the epidemic threshold.Finally,conclusions are drawn through numerical modeling and analysis.The conclusions suggest that when negative information increases,the probability of the transmission of anxious states across the population increases.The transmission of anxious states increases the final size of the disease and decreases its outbreak threshold.Reducing the impact of environmental factors at both the informational and emotional levels is beneficial for controlling the scale of the spread of the epidemic.Our findings can provide a reference for improving public health awareness and behavioral decision-making,mitigating the adverse impacts of anxious states,and ultimately controlling the spread of epidemics.
基金supported by the National Natural Science Foundation of China(No.62027801).
文摘In modern wireless communication and electromagnetic control,automatic modulationclassification(AMC)of orthogonal frequency division multiplexing(OFDM)signals plays animportant role.However,under Doppler frequency shift and complex multipath channel conditions,extracting discriminative features from high-order modulation signals and ensuring model inter-pretability remain challenging.To address these issues,this paper proposes a Fourier attention net-work(FAttNet),which combines an attention mechanism with a Fourier analysis network(FAN).Specifically,the method directly converts the input signal to the frequency domain using the FAN,thereby obtaining frequency features that reflect the periodic variations in amplitude and phase.Abuilt-in attention mechanism then automatically calculates the weights for each frequency band,focusing on the most discriminative components.This approach improves both classification accu-racy and model interpretability.Experimental validation was conducted via high-order modulationsimulation using an RF testbed.The results show that under three different Doppler frequencyshifts and complex multipath channel conditions,with a signal-to-noise ratio of 10 dB,the classifi-cation accuracy can reach 89.1%,90.4%and 90%,all of which are superior to the current main-stream methods.The proposed approach offers practical value for dynamic spectrum access and sig-nal security detection,and it makes important theoretical contributions to the application of deeplearning in complex electromagnetic signal recognition.
基金supported by the National Natural Science Foundation of China(Grant No.12002135)the Natural Science Foundation of Jiangsu Province(Grant No.BK20190836)+2 种基金China Postdoctoral Science Foundation(Grant No.2019M661732)the Natural Science Research of Jiangsu Higher Education Institutions of China(Grant No.19KJB110001)Priority Academic Program Developmentof Jiangsu Higher Education Institutions(Grant No.PAPD-2018-87).
文摘The coronavirus disease 2019(COVID-19)has been widely spread around the world,and the control and behavior dynamics are still one of the important research directions in the world.Based on the characteristics of COVID-19’s spread,a coupled disease-awareness model on multiplex networks is proposed in this paper to study and simulate the interaction between the spreading behavior of COVID-19 and related information.In the layer of epidemic spreading,the nodes can be divided into five categories,where the topology of the network represents the physical contact relationship of the population.The topological structure of the upper network shows the information interaction among the nodes,which can be divided into aware and unaware states.Awareness will make people play a positive role in preventing the epidemic diffusion,influencing the spread of the disease.Based on the above model,we have established the state transition equation,through the microscopic Markov chain approach(MMCA),and proposed the propagation threshold calculation method under the epidemic model.Furthermore,MMCA iteration and the Monte Carlo method are simulated on the static network and dynamic network,respectively.The current results will be beneficial to the study of COVID-19,and propose a more rational and effective model for future research on epidemics.
基金Project supported by the National Natural Science Foundation of China (Grant Nos. 72174121 and 71774111)the Program for Professor of Special Appointment (Eastern Scholar) at Shanghai Institutions of Higher Learningthe Natural Science Foundation of Shanghai (Grant No. 21ZR1444100)。
文摘In recent years, the impact of information diffusion and individual behavior adoption patterns on epidemic transmission in complex networks has received significant attention. In the immunization behavior adoption process, different individuals often make behavioral decisions in different ways, and it is of good practical importance to study the influence of individual heterogeneity on the behavior adoption process. In this paper, we propose a three-layer coupled model to analyze the process of co-evolution of official information diffusion, immunization behavior adoption and epidemic transmission in multiplex networks, focusing on individual heterogeneity in behavior adoption patterns. Specifically, we investigate the impact of the credibility of social media and the risk sensitivity of the population on behavior adoption in further study of the effect of heterogeneity of behavior adoption on epidemic transmission. Then we use the microscopic Markov chain approach to describe the dynamic process and capture the evolution of the epidemic threshold. Finally, we conduct extensive simulations to prove our findings. Our results suggest that enhancing the credibility of social media can raise the epidemic transmission threshold, making it effective at controlling epidemic transmission during the dynamic process. In addition, improving an individuals' risk sensitivity, and thus their taking effective protective measures, can also reduce the number of infected individuals and delay the epidemic outbreak. Our study explores the role of individual heterogeneity in behavior adoption in real networks, more clearly models the effect of the credibility of social media and risk sensitivity of the population on the epidemic transmission dynamic, and provides a useful reference for managers to formulate epidemic control and prevention policies.
基金the National Natural Science Foundation of China(Grant Nos.11601294 and 61873154),Shanxi Scholarship Council of China(Grant No.2016-011)the Shanxi Province Science Foundation for Youths(Grant Nos.201601D021012,201801D221011,201901D211159,201801D221007 and 201801D221003)the 1331 Engineering Project of Shanxi Province,China.
文摘Over the last few years,the interplay between contagion dynamics of social influences(e.g.,human awareness,risk perception,and information dissemination)and biological infections has been extensively investigated within the framework of multiplex networks.The vast majority of existing multiplex network spreading models typically resort to heterogeneous mean-field approximation and microscopic Markov chain approaches.Such approaches usually manifest richer dynamical properties on multiplex networks than those on simplex networks;however,they fall short of a subtle analysis of the variations in connections between nodes of the network and fail to account for the adaptive behavioral changes among individuals in response to epidemic outbreaks.To transcend these limitations,in this paper we develop a highly integrated effective degree approach to modeling epidemic and awareness spreading processes on multiplex networks coupled with awareness-dependent adaptive rewiring.This approach keeps track of the number of nearest neighbors in each state of an individual;consequently,it allows for the integration of changes in local contacts into the multiplex network model.We derive a formula for the threshold condition of contagion outbreak.Also,we provide a lower bound for the threshold parameter to indicate the effect of adaptive rewiring.The threshold analysis is confirmed by extensive simulations.Our results show that awareness-dependent link rewiring plays an important role in enhancing the transmission threshold as well as lowering the epidemic prevalence.Moreover,it is revealed that intensified awareness diffusion in conjunction with enhanced link rewiring makes a greater contribution to disease prevention and control.In addition,the critical phenomenon is observed in the dependence of the epidemic threshold on the awareness diffusion rate,supporting the metacritical point previously reported in literature.This work may shed light on understanding of the interplay between epidemic dynamics and social contagion on adaptive networks.
基金supported in part by NSFC project (61571058, 61601052)
文摘Spatial division multiplexing enabled elastic optical networks(SDM-EONs) are the potential implementation form of future optical transport networks, because it can curve the physical limitation of achievable transmission capacity in single-mode fiber and single-core fiber. However, spectrum fragmentation issue becomes more serious in SDM-EONs compared with simple elastic optical networks(EONs) with single mode fiber or single core fiber. In this paper, multicore virtual concatenation(MCVC) scheme is first proposed considering inter-core crosstalk to solve the spectrum fragmentation issue in SDM-EONs. Simulation results show that the proposed MCVC scheme can achieve better performance compared with the baseline scheme, i.e., single-core virtual concatenation(SCVC) scheme, in terms of blocking probability and spectrum utilization.
基金This work is supported in part by the National Natural Science Foundation of China under Grant No.61672022.
文摘The problem of influence maximizing in social networks refers to obtaining a set of nodes of a specified size under a specific propagation model so that the aggregation of the node-set in the network has the greatest influence.Up to now,most of the research has tended to focus on monolayer network rather than on multiplex networks.But in the real world,most individuals usually exist in multiplex networks.Multiplex networks are substantially different as compared with those of a monolayer network.In this paper,we integrate the multi-relationship of agents in multiplex networks by considering the existing and relevant correlations in each layer of relationships and study the problem of unbalanced distribution between various relationships.Meanwhile,we measure the distribution across the network by the similarity of the links in the different relationship layers and establish a unified propagation model.After that,place on the established multiplex network propagation model,we propose a basic greedy algorithm on it.To reduce complexity,we combine some of the characteristics of triggering model into our algorithm.Then we propose a novel MNStaticGreedy algorithm which is based on the efficiency and scalability of the StaticGreedy algorithm.Our experiments show that the novel model and algorithm are effective,efficient and adaptable.
基金This work was supported by the National Natural Science Foundation of China(NSFC)under Grant U19B2004in part by National Key R&D Program of China under Grant 2022YFB2901202+1 种基金in part by the Open Funding Projects of the State Key Laboratory of Communication Content Cognition(No.20K05 and No.A02107)in part by the Special Fund for Science and Technology of Guangdong Province under Grant 2019SDR002.
文摘For networking of big data applications,an essential issue is how to represent networks in vector space for further mining and analysis tasks,e.g.,node classification,clustering,link prediction,and visualization.Most existing studies on this subject mainly concentrate on monoplex networks considering a single type of relation among nodes.However,numerous real-world networks are naturally composed of multiple layers with different relation types;such a network is called a multiplex network.The majority of existing multiplex network embedding methods either overlook node attributes,resort to node labels for training,or underutilize underlying information shared across multiple layers.In this paper,we propose Multiplex Network Infomax(MNI),an unsupervised embedding framework to represent information of multiple layers into a unified embedding space.To be more specific,we aim to maximize the mutual information between the unified embedding and node embeddings of each layer.On the basis of this framework,we present an unsupervised network embedding method for attributed multiplex networks.Experimental results show that our method achieves competitive performance on not only node-related tasks,such as node classification,clustering,and similarity search,but also a typical edge-related task,i.e.,link prediction,at times even outperforming relevant supervised methods,despite that MNI is fully unsupervised.
基金partially supported by the Project for the National Natural Science Foundation of China(72174121,71774111)the Program for Professor of Special Appointment(Eastern Scholar)at Shanghai Institutions of Higher Learning+2 种基金the Project for the Natural Science Foundation of Shanghai(21ZR1444100)Project soft science research of Shanghai(22692112600)National Social Science Foundation of China(21BGL217,22BGL240)。
文摘Disease is a serious threat to human society.Understanding the characteristics of disease transmission is helpful for people to effectively control disease.In real life,it is natural to take various measures when people are aware of disease.In this paper,a novel coupled model considering asymmetric activity is proposed to describe the interactions between information diffusion and disease transmission in multiplex networks.Then,the critical threshold for disease transmission is derived by using the micro-Markov chain method.Finally,the theoretical results are verified by numerical simulations.The results show that reducing the activity level of individuals in the physical contact layer will have a continuous impact on reducing the disease outbreak threshold and suppressing the disease.In addition,the activity level of individuals in the virtual network has little impact on the transmission of the disease.Meanwhile,when individuals are aware of more disease-related information,the higher their awareness of prevention will be,which can effectively inhibit the transmission of disease.Our research results can provide a useful reference for the control of disease transmission.
基金support by the National Natural Science Foundation of China(NSFC)under grant number 61873274.
文摘Contrastive self‐supervised representation learning on attributed graph networks with Graph Neural Networks has attracted considerable research interest recently.However,there are still two challenges.First,most of the real‐word system are multiple relations,where entities are linked by different types of relations,and each relation is a view of the graph network.Second,the rich multi‐scale information(structure‐level and feature‐level)of the graph network can be seen as self‐supervised signals,which are not fully exploited.A novel contrastive self‐supervised representation learning framework on attributed multiplex graph networks with multi‐scale(named CoLM^(2)S)information is presented in this study.It mainly contains two components:intra‐relation contrast learning and interrelation contrastive learning.Specifically,the contrastive self‐supervised representation learning framework on attributed single‐layer graph networks with multi‐scale information(CoLMS)framework with the graph convolutional network as encoder to capture the intra‐relation information with multi‐scale structure‐level and feature‐level selfsupervised signals is introduced first.The structure‐level information includes the edge structure and sub‐graph structure,and the feature‐level information represents the output of different graph convolutional layer.Second,according to the consensus assumption among inter‐relations,the CoLM^(2)S framework is proposed to jointly learn various graph relations in attributed multiplex graph network to achieve global consensus node embedding.The proposed method can fully distil the graph information.Extensive experiments on unsupervised node clustering and graph visualisation tasks demonstrate the effectiveness of our methods,and it outperforms existing competitive baselines.
基金ACKNOWLEDGEMENT This work was supported in part by the Na- tional Natural Science Foundation of China un- der Grants No. 60972072, No. 61340033 and the 111 Project of China under Grant No. B08038.
文摘This paper presents a novel spec- trum sharing design aiming at optimising the performance of a Multiuser Orthogonal Freq- uency-Division Multiplexing （MU-OFDM） Co- gnitive Radio Network （CRN） that consists of multiple secondary Transmitter-Receiver （Tx-Rx） pairs. For most MU-OFDM systems, the Exc- lusive Subchannel Assignment （ESA） is an efficient resource allocation method. Noneth- eless, it is inappropriate for the network consi- dered in this paper, because subchannels shar- ing among secondary Tx-Rx pairs can further improve the system performance. We investi- gate the Weighted Sum Rate （WSR） maximi- zation problem under the Shared Subchannel Assignment （SSA）, where each subchannel is shared by multiple secondary Tx-Rx pairs. With Lagrangian duality technique, we decompose the original resource allocation problem into sev- eral sub-problems on each subchannel and pro- pose a duality-based suhchannel sharing ap- proach. For practical realisation in the cogni- tive systems without central control entity, a distributed duality-based WSR maximization scheme is presented. Simulation results mani- fest that the proposed scheme achieves sig- nificantly better performance than ESA duality scheme.
文摘As the main food source for humans, the global movement of the three major grains significantly impacts human survival and development. To investigate the evolution of the world cereal trade network and its development trend, a weighted directed dynamic multiplexed network was established using historical data on cereal trade, cereal import dependency ratio, and arable land per capita. Inspired by the MLP framework, we redefined the weight determination method for computing layer weights and edge weights of the target layer, modified the CN, RA, AA, and PA indicators, and proposed the node similarity indicator for weighted directed networks. The AUC metric, which measures the accuracy of the algorithm, has also been improved in order to finally obtain the link prediction results for the grain trading network. The prediction results were processed, such as web-based presentation and community partition. It was found that the number of generalized trade agreements does not have a decisive impact on inter-country cereal trade. The former large grain exporters continue to play an important role in this trade network. In the future, the world trade in cereals will develop in the direction of more frequent intercontinental trade and gradually weaken the intracontinental cereal trade.
基金supported by the National Natural Science Foundation of China (Grant No.62135009)the Beijing Municipal Science and Technology Commission,Administrative Commission of Zhongguancun Science Park (Grant No.Z221100005322010).
文摘On-chip diffractive optical neural networks(DONNs)bring the advantages of parallel processing and low energy consumption.However,an accurate representation of the optical field’s evolution in the structure cannot be provided using the previous diffraction-based analysis method.Moreover,the loss caused by the open boundaries poses challenges to applications.A multimode DONN architecture based on a more precise eigenmode analysis method is proposed.We have constructed a universal library of input,output,and metaline structures utilizing this method,and realized a multimode DONN composed of the structures from the library.On the designed multimode DONNs with only one layer of the metaline,the classification task of an Iris plants dataset is verified with an accuracy of 90%on the blind test dataset,and the performance of the one-bit binary adder task is also validated.Compared to the previous architectures,the multimode DONN exhibits a more compact design and higher energy efficiency.
基金the Alexander von Humboldt Foundation for their support。
文摘Future high-speed mobile communication systems require low latency and high capacity networks.Coherent wavelength division multiplexing(WDM)passive optical network(PON)scheme is expected to play a vital role in these systems.In this paper,coherent WDM-PON scheme based on dual-polarization 16-quadrature amplitude modulation(DP-16 QAM)transceiver has been investigated.The aim of this scheme is to build a 2 Tbit/s(125 Gbit/s/λ×16 wavelengths)network that will be used in the construction of the transport architecture of fifth generation(5 G)and beyond 5 G(B5 G)cellular networks either in mobile front haul(MFH)or mobile back haul(MBH).The results indicate that the proposed scheme is very adequate for both 5 G and B5 G cellular networks requirements.
文摘As a promising solution, virtualization is vigorously developed to eliminate the ossification of traditional Internet infrastructure and enhance the flexibility in sharing the substrate network (SN) resources including computing, storage, bandwidth, etc. With network virtualization, cloud service providers can utilize the shared substrate resources to provision virtual networks (VNs) and facilitate a wide and diverse range of applications. As more and more internet applications migrate to the cloud, the resource efficiency and the survivability of VNs, such as single link failure or large-scale disaster survivability, have become crucial issues. Elastic optical networks have emerged in recent years as a strategy for dealing with the divergence of network application bandwidth needs. The network capacity has been constrained due to the usage of only two multiplexing dimensions. As transmission rates rise, so does the demand for network failure protection. Due to their end-to-end solutions, those safe-guarding paths are of particular importance among the protection methods. Due to their end-to-end solutions, those safeguarding paths are of particular importance among the protection methods. This paper presents approaches that provide a failure-independent route-protecting p-cycle for path protection in space-division multiplexed elastic optical networks. This letter looks at two SDM network challenges and presents a heuristic technique (k-shortest path) for each. In the first approach, we study a virtual network embedding (SVNE) problem and propose an algorithm for EONs, which can combat against single-link failures. We evaluate the proposed POPETA algorithm and compare its performance with some counterpart algorithms. Simulation results demonstrate that the proposed algorithm can achieve satisfactory performance in terms of spectrum utilization and blocking ratio, even if with a higher backup redundancy ratio.
文摘With the rise of cloud computing in recent years, a large number of streaming media has yielded an exponential growth in network traffic. With the now present 5G and future 6G, the development of the Internet of Things (IoT), social networks, video on demand, and mobile multimedia platforms, the backbone network is bound to bear more traffic. The transmission capacity of Single Core Fiber (SCFs) may be limited in the future and Spatial Division Multiplexing (SDM) leveraging multi-core fibers promises to be one of the solutions for the future. Currently, Elastic optical networks (EONs) with multi-core fibers (MCFs) are a kind of SDM-enabled EONs (SDM-EON) used to enhance the capacity of transmission. The resource assignment in MCFs, however, will be subject to Inter-Core Crosstalk (IC-XT), hence, reducing the effectiveness of transmission. This research highlights the routing, modulation level, and spectrum assignment (RMLSA) problems with anycast traffic mode in SDM-EON. A multipath routing scheme is used to reduce the blocking rate of anycast traffic in SDM-EON with the limit of inter-core crosstalk. Hence, an integer linear programming (ILP) problem is formulated and a heuristic algorithm is proposed. Two core-assignment strategies: First-Fit (FF) and Random-Fit (RF) are used and their performance is evaluated through simulations. The simulation results show that the multipath routing method is better than the single-path routing method in terms of blocking ratio and spectrum utilization ratio. Moreover, the FF is better than the RF in low traffic load in terms of blocking ratio (BR), and the opposite in high traffic load. The FF is better than the RF in terms of a spectrum utilization ratio. In an anycast protection problem, the proposed algorithm has a lower BR than previous works.
文摘A passive optical network （PON） scheme based on optical code division multiplexing （OCDM） for the downstream traffics is proposed and analyzed in detail. In the PON, the downstream traffics are broadcasted by OCDM technology to guarantee the security, while the upstream traffics pass through the same optical fiber by the common time division multiple access （TDMA） technology to decrease the cost. This schemes are denoted as OCDM/TDMA-PON, which can be applied to an optical access network （OAN） with full services on demand, such as Internet protocol, video on demand, tele-presence and high quality audio. The proposed OCDM/TDMA-PON scheme combines advantages of PON, TDMA, and OCDM technology. Simulation results indicate that the designed scheme improves the OAN performance, and enhances flexibility and scalability of the system.
基金The National Key Basic Research and Department Program of China(No.2013CB329606)
文摘In complex network of real world, there are many types of relationships between individuals, and the more effective research ways for this kind of network is to abstract these relationship as a multiplex network. More and more researchers are attracted to be engaged in multiplex network research. A novel framework of community detection of multiplex network based on consensus matrix was presented. Firstly, this framework merges the structure of multiplex network and the information of link between each node into monoplex network. Then, the community structure information of each layer network was obtained through consensus matrix, and the traditional community division algorithm was utilized to carry out community detection of combine networks. The experimental results show that the proposed algorithm can get better performance of community partition in the real network datasets.
