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
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
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
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
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
Official and civil information, as distinct information sources, significantly influence public behavior and the dynamics of epidemic transmission. In this paper, we propose a three-layer U_(1)A_(1)U_(1)-U_(2)A_(2)U_(...Official and civil information, as distinct information sources, significantly influence public behavior and the dynamics of epidemic transmission. In this paper, we propose a three-layer U_(1)A_(1)U_(1)-U_(2)A_(2)U_(2)-SIS coupled model to analyze the co-evolution process of official information dissemination, civil information dissemination and epidemic transmission,considering the interdependencies between the information dissemination channels. The first layer describes the official information dissemination process. The second layer models the civil information dissemination process, considering the effects of perceived risk costs and the role of the correlation between official and civil information. The third layer represents the epidemic transmission process, highlighting the impact of the correlation between official and civil information on epidemic transmission. Then, using the microscopic Markov chain approach, we describe the information-epidemic coupled dynamics and derive the epidemic outbreak threshold. Our research demonstrates that a stronger positive correlation between official and civil information raises the epidemic threshold and suppresses the scale of epidemic transmission. Furthermore, individuals' adoption of civil information should involve a more thorough assessment of the infection risk based on their personal circumstances, which can contribute to more effective epidemic control. Moreover, enhancing infected individuals' accurate comprehension of official information can effectively curb the transmission of the epidemic. Our study highlights the importance of both official and civil information dissemination in epidemic management and provides insights for policymakers in developing effective public health and communication strategies.展开更多
Demand response has recently become an essential means for businesses to reduce production costs in industrial chains.Meanwhile,the current industrial chain structure has also become increasingly complex,forming new c...Demand response has recently become an essential means for businesses to reduce production costs in industrial chains.Meanwhile,the current industrial chain structure has also become increasingly complex,forming new characteristics of multiplex networked industrial chains.Fluctuations in real-time electricity prices in demand response propagate through the coupling and cascading relationships within and among these network layers,resulting in negative impacts on the overall energy management cost.However,existing demand response methods based on reinforcement learning typically focus only on individual agents without considering the influence of dynamic factors on intra and inter-network relationships.This paper proposes a Layered Temporal Spatial Graph Attention(LTSGA)reinforcement learning algorithm suitable for demand response in multiplex networked industrial chains to address this issue.The algorithm first uses Long Short-Term Memory(LSTM)to learn the dynamic temporal characteristics of electricity prices for decision-making.Then,LTSGA incorporates a layered spatial graph attention model to evaluate the impact of dynamic factors on the complex multiplex networked industrial chain structure.Experiments demonstrate that the proposed LTSGA approach effectively characterizes the influence of dynamic factors on intra-and inter-network relationships within the multiplex industrial chain,enhancing convergence speed and algorithm performance compared with existing state-of-the-art algorithms.展开更多
With the advancement of electronic information technology and the growth of the intelligent industry,the industrial sector has undergone a shift from simplex,linear,and vertical chains to complex,multi-level,and multi...With the advancement of electronic information technology and the growth of the intelligent industry,the industrial sector has undergone a shift from simplex,linear,and vertical chains to complex,multi-level,and multi-dimensional networked industrial chains.In order to enhance energy efficiency in multiplex networked industrial chains under time-of-use price,a coarse time granularity task scheduling approach has been adopted.This approach adjusts the distribution of electricity supply based on task deadlines,dividing it into longer periods to facilitate batch access to task information.However,traditional simplex-network task assignment optimization methods are unable to achieve a globally optimal solution for cross-layer links in multiplex networked industrial chains.Existing solutions struggle to balance execution costs and completion efficiency in time-of-use price scenarios.Therefore,this paper presents a mixed-integer linear programming model for solving the problem scenario and two algorithms:an exact algorithm based on the branch-and-bound method and a multi-objective heuristic algorithm based on cross-layer policy propagation.These algorithms are designed to adapt to small-scale and large-scale problem scenarios under coarse time granularity.Through extensive simulation experiments and theoretical analysis,the proposed methods effectively optimize the energy and time costs associated with the task execution.展开更多
There have been increasing interests in studying multiplex dynamical networks recently.This paper focuses on topology identiflcation of two-layer multiplex networks with peer-to-peer interlayer couplings.For a two-lay...There have been increasing interests in studying multiplex dynamical networks recently.This paper focuses on topology identiflcation of two-layer multiplex networks with peer-to-peer interlayer couplings.For a two-layer network model in which different layers have different coupling patterns,we propose novel methods to recover unknown topological structure of one layer,using the information of the other layer known as a prior.The proposed methods make full use of the measured evolutional states of the multiplex network itself,and treat the layer with a known structure as an auxiliary layer which is designed to identify the unknown topological layer.Compared with the traditional synchronization-based identiflcation method,the proposed methods are in no need of constructing an additional auxiliary network to identify the unknown topological layer,and thus greatly reduce the cost of topology identiflcation.Finally,numerical simulations validate the effectiveness of the proposed methods.展开更多
The pandemic of COVID-19 witnessed a massive infodemic with the public being bombarded with vast quantities of information.The spreading of neutral and highly accurate reports can guide the public to self-protect and ...The pandemic of COVID-19 witnessed a massive infodemic with the public being bombarded with vast quantities of information.The spreading of neutral and highly accurate reports can guide the public to self-protect and reduce the pandemic.Mis-and dis-information would intrigue panic and high exposure risk to epidemic.Although the infodemic has attracted attentions from the academia,it is still not known to what degree and in which direction the information flows contribute to the COVID-19 pandemic.To fill the gap,we apply network reconstruction techniques to rebuild the hidden multiplex network of information and COVID-19 spreading by which we aim at quantifying the interaction between the propagation of information and the spatial outbreak of COVID-19,and delineate between the positive and negative impact of information on the pandemic.By differentiating the types of media that participated in the information process,we find that in the early stage of COVID-19 pandemic,infodemic does play a critical role to amplify the risk of virus outbreak in China and the risk is even larger for those highly developed regions.Compared to the old-fashion media,the new mobile platforms impose a greater risk to reinforce the positive feedback between infodemic and COVID-19 pandemic.展开更多
Identifying cancer driver genes has paramount significance in elucidating the intricate mechanisms underlying cancer development,progression,and therapeutic interventions.Abundant omics data and interactome networks p...Identifying cancer driver genes has paramount significance in elucidating the intricate mechanisms underlying cancer development,progression,and therapeutic interventions.Abundant omics data and interactome networks provided by numerous extensive databases enable the application of graph deep learning techniques that incorporate network structures into the deep learning framework.However,most existing models primarily focus on individual network,inevitably neglecting the incompleteness and noise of interactions.Moreover,samples with imbalanced classes in driver gene identification hamper the performance of models.To address this,we propose a novel deep learning framework MMGN,which integrates multiplex networks and pan-cancer multiomics data using graph neural networks combined with negative sample inference to discover cancer driver genes,which not only enhances gene feature learning based on the mutual information and the consensus regularizer,but also achieves balanced class of positive and negative samples for model training.The reliability of MMGN has been verified by the Area Under the Receiver Operating Characteristic curves(AUROC)and the Area Under the Precision-Recall Curves(AUPRC).We believe MMGN has the potential to provide new prospects in precision oncology and may find broader applications in predicting biomarkers for other intricate diseases.展开更多
In this paper,a meaningful representation of the road network using multiplex networks and a novel feature selection framework that enhances the predictability of future traffic conditions of an entire network are pro...In this paper,a meaningful representation of the road network using multiplex networks and a novel feature selection framework that enhances the predictability of future traffic conditions of an entire network are proposed.Using data on traffic volumes and tickets’validation from the transportation network of Athens,we were able to develop prediction models that not only achieve very good performance but are also trained efficiently,do not introduce high complexity and,thus,are suitable for real-time operation.More specifically,the network’s nodes(loop detectors and subway/metro stations)are organized as a multilayer graph,each layer representing an hour of the day.Nodes with similar structural properties are then classified in communities and are exploited as features to predict the future demand values of nodes belonging to the same community.The results reveal the potential of the proposed method to provide reliable and accurate predictions.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
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
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
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
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.
基金partially supported by the Project for the National Natural Science Foundation of China (72174121)the Project Soft Science Research of Shanghai (24692116300)the Program for Professor of Special Appointment (Eastern Scholar) at Shanghai Institutions of Higher Learning。
文摘Official and civil information, as distinct information sources, significantly influence public behavior and the dynamics of epidemic transmission. In this paper, we propose a three-layer U_(1)A_(1)U_(1)-U_(2)A_(2)U_(2)-SIS coupled model to analyze the co-evolution process of official information dissemination, civil information dissemination and epidemic transmission,considering the interdependencies between the information dissemination channels. The first layer describes the official information dissemination process. The second layer models the civil information dissemination process, considering the effects of perceived risk costs and the role of the correlation between official and civil information. The third layer represents the epidemic transmission process, highlighting the impact of the correlation between official and civil information on epidemic transmission. Then, using the microscopic Markov chain approach, we describe the information-epidemic coupled dynamics and derive the epidemic outbreak threshold. Our research demonstrates that a stronger positive correlation between official and civil information raises the epidemic threshold and suppresses the scale of epidemic transmission. Furthermore, individuals' adoption of civil information should involve a more thorough assessment of the infection risk based on their personal circumstances, which can contribute to more effective epidemic control. Moreover, enhancing infected individuals' accurate comprehension of official information can effectively curb the transmission of the epidemic. Our study highlights the importance of both official and civil information dissemination in epidemic management and provides insights for policymakers in developing effective public health and communication strategies.
基金supported by the National Key Research and Development Program of China(No.2022YFB3304400)the National Natural Science Foundation of China(Nos.62303111,62076060,and 61932007)+2 种基金the Key Research and Development Program of Jiangsu Province of China(No.BE2022157)the Defense Industrial Technology Development Program(No.JCKY2021214B002)the Fellowship of China Postdoctoral Science Foundation(No.2022M720715).
文摘Demand response has recently become an essential means for businesses to reduce production costs in industrial chains.Meanwhile,the current industrial chain structure has also become increasingly complex,forming new characteristics of multiplex networked industrial chains.Fluctuations in real-time electricity prices in demand response propagate through the coupling and cascading relationships within and among these network layers,resulting in negative impacts on the overall energy management cost.However,existing demand response methods based on reinforcement learning typically focus only on individual agents without considering the influence of dynamic factors on intra and inter-network relationships.This paper proposes a Layered Temporal Spatial Graph Attention(LTSGA)reinforcement learning algorithm suitable for demand response in multiplex networked industrial chains to address this issue.The algorithm first uses Long Short-Term Memory(LSTM)to learn the dynamic temporal characteristics of electricity prices for decision-making.Then,LTSGA incorporates a layered spatial graph attention model to evaluate the impact of dynamic factors on the complex multiplex networked industrial chain structure.Experiments demonstrate that the proposed LTSGA approach effectively characterizes the influence of dynamic factors on intra-and inter-network relationships within the multiplex industrial chain,enhancing convergence speed and algorithm performance compared with existing state-of-the-art algorithms.
基金supported by the National Key Research and Development Program of China(No.2022YFB3304400)the National Natural Science Foundation of China(Nos.62303111,62076060,and 61932007)+2 种基金the Key Research and Development Program of Jiangsu Province of China(No.BE2022157)the Defense Industrial Technology Development Program(No.JCKY2021214B002)the Fellowship of China Postdoctoral Science Foundation(No.2022M720715).
文摘With the advancement of electronic information technology and the growth of the intelligent industry,the industrial sector has undergone a shift from simplex,linear,and vertical chains to complex,multi-level,and multi-dimensional networked industrial chains.In order to enhance energy efficiency in multiplex networked industrial chains under time-of-use price,a coarse time granularity task scheduling approach has been adopted.This approach adjusts the distribution of electricity supply based on task deadlines,dividing it into longer periods to facilitate batch access to task information.However,traditional simplex-network task assignment optimization methods are unable to achieve a globally optimal solution for cross-layer links in multiplex networked industrial chains.Existing solutions struggle to balance execution costs and completion efficiency in time-of-use price scenarios.Therefore,this paper presents a mixed-integer linear programming model for solving the problem scenario and two algorithms:an exact algorithm based on the branch-and-bound method and a multi-objective heuristic algorithm based on cross-layer policy propagation.These algorithms are designed to adapt to small-scale and large-scale problem scenarios under coarse time granularity.Through extensive simulation experiments and theoretical analysis,the proposed methods effectively optimize the energy and time costs associated with the task execution.
基金supported by the National Natural Science Foundation of China(Grant Nos.62176099,61773175,61936004,and 61973241)。
文摘There have been increasing interests in studying multiplex dynamical networks recently.This paper focuses on topology identiflcation of two-layer multiplex networks with peer-to-peer interlayer couplings.For a two-layer network model in which different layers have different coupling patterns,we propose novel methods to recover unknown topological structure of one layer,using the information of the other layer known as a prior.The proposed methods make full use of the measured evolutional states of the multiplex network itself,and treat the layer with a known structure as an auxiliary layer which is designed to identify the unknown topological layer.Compared with the traditional synchronization-based identiflcation method,the proposed methods are in no need of constructing an additional auxiliary network to identify the unknown topological layer,and thus greatly reduce the cost of topology identiflcation.Finally,numerical simulations validate the effectiveness of the proposed methods.
基金supported by National Natural Science Foundation of China[61673151]the Ministry of Education in China Project of Humanities and Social Sciences[20YJC790176]+1 种基金Zhejiang Provincial Natural Science Foundation of China[LR18A050001]the Science and Technology Key Project of Xinjiang Production and Construction Corps,and the Major Project of The National Social Science Fund of China[19ZDA324].
文摘The pandemic of COVID-19 witnessed a massive infodemic with the public being bombarded with vast quantities of information.The spreading of neutral and highly accurate reports can guide the public to self-protect and reduce the pandemic.Mis-and dis-information would intrigue panic and high exposure risk to epidemic.Although the infodemic has attracted attentions from the academia,it is still not known to what degree and in which direction the information flows contribute to the COVID-19 pandemic.To fill the gap,we apply network reconstruction techniques to rebuild the hidden multiplex network of information and COVID-19 spreading by which we aim at quantifying the interaction between the propagation of information and the spatial outbreak of COVID-19,and delineate between the positive and negative impact of information on the pandemic.By differentiating the types of media that participated in the information process,we find that in the early stage of COVID-19 pandemic,infodemic does play a critical role to amplify the risk of virus outbreak in China and the risk is even larger for those highly developed regions.Compared to the old-fashion media,the new mobile platforms impose a greater risk to reinforce the positive feedback between infodemic and COVID-19 pandemic.
基金supported in part by the National Natural Science Foundation of China(No.62202383)the Guangdong Basic and Applied Basic Research Foundation(No.2024A1515012602)the National Key Research and Development Program of China(No.2022YFD1801200).
文摘Identifying cancer driver genes has paramount significance in elucidating the intricate mechanisms underlying cancer development,progression,and therapeutic interventions.Abundant omics data and interactome networks provided by numerous extensive databases enable the application of graph deep learning techniques that incorporate network structures into the deep learning framework.However,most existing models primarily focus on individual network,inevitably neglecting the incompleteness and noise of interactions.Moreover,samples with imbalanced classes in driver gene identification hamper the performance of models.To address this,we propose a novel deep learning framework MMGN,which integrates multiplex networks and pan-cancer multiomics data using graph neural networks combined with negative sample inference to discover cancer driver genes,which not only enhances gene feature learning based on the mutual information and the consensus regularizer,but also achieves balanced class of positive and negative samples for model training.The reliability of MMGN has been verified by the Area Under the Receiver Operating Characteristic curves(AUROC)and the Area Under the Precision-Recall Curves(AUPRC).We believe MMGN has the potential to provide new prospects in precision oncology and may find broader applications in predicting biomarkers for other intricate diseases.
文摘In this paper,a meaningful representation of the road network using multiplex networks and a novel feature selection framework that enhances the predictability of future traffic conditions of an entire network are proposed.Using data on traffic volumes and tickets’validation from the transportation network of Athens,we were able to develop prediction models that not only achieve very good performance but are also trained efficiently,do not introduce high complexity and,thus,are suitable for real-time operation.More specifically,the network’s nodes(loop detectors and subway/metro stations)are organized as a multilayer graph,each layer representing an hour of the day.Nodes with similar structural properties are then classified in communities and are exploited as features to predict the future demand values of nodes belonging to the same community.The results reveal the potential of the proposed method to provide reliable and accurate predictions.
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.
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.
