Impact of individual behavior adoption heterogeneity on epidemic transmission in multiplex networks

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.

Multiplex network infomax:Multiplex network embedding via information fusion

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.

Contagion dynamics on adaptive multiplex networks with awareness-dependent rewiring

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.

Influence Diffusion Model in Multiplex Networks

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.

Community detection in multiplex networks via consensus matrix

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.

Impact of asymmetric activity on interactions between information diffusion and disease transmission in multiplex networks

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.

CoLM^(2)S:Contrastive self‐supervised learning on attributed multiplex graph network with multi‐scale information

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.

Research on Weighted Directed Dynamic Multiplexing Network of World Grain Trade Based on Improved MLP Framework

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.

Multiplex network reconstruction for the coupled spatial diffusion of infodemic and pandemic of COVID-19

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.

Modeling the COVID-19 epidemic and awareness diffusion on multiplex networks

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.

Recovering unknown topology in a two-layer multiplex network:One layer infers the other layer

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.

Multi-Core Virtual Concatenation Scheme Considering Inter-Core Crosstalk in Spatial Division Multiplexing Enabled Elastic Optical Networks

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.

Minimizing Traffic Blocking and Inter-Crosstalk in Spatial Division Multiplexing over Elastic Optical Networking

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.

基于多层网络建模的在线学习社区群体动力激活研究

在线学习社区的群体动力激活有助于促进多元复杂背景的学习者进行深度联通与知识创新。通过挖掘与整合学习者、概念、话题在复杂网络中的量化指标来构建群体动力激活机制,进而为在线学习社区群体互动与持续发展提供思路建议。基于多层网络分析方法中的多路复用网络和多维型多层网络,构建了社会交互、认知共现、概念关联和话题关联四层关系网络的多层网络模型,并设计了包含学习同伴、学习资源、学习话题推荐策略的群体动力激活机制。以联通主义课程cMOOC 5.0为应用情境,验证了该多层网络模型应用于在线学习社区的有效性,并根据建模结果提出具体的群体动力激活机制:基于社会交互与认知共现网络推荐学习同伴以强化群体凝聚力;基于概念关联网络推荐学习资源以提升群体驱动力;基于概念关联和话题关联网络推荐学习话题以降低群体耗散力。继而将多层网络建模的研究价值从微观层面的学习规律挖掘扩展至中观层面的教学干预设计。

D2D通信复用异构蜂窝网络中分布式路由资源分配方法

由于设备到设备(Device-To-Device,D2D)通信复用异构蜂窝网络环境的复杂性和动态性,难以实现高效、均衡的路由资源利用,文章提出D2D通信复用异构蜂窝网络中分布式路由资源分配方法。采用分布式系统模型构建D2D通信复用异构蜂窝网络的拓扑结构,基于邻居节点信息交换实现D2D设备发现,并引入自适应的会话建立策略建立通信链路,采用贪婪算法为D2D用户分配子信道,实现路由资源的均衡分配。实验结果表明,设计方法在提升D2D用户之间数据传输速率方面具有较好的性能,适用于D2D通信复用异构蜂窝网络中的路由资源分配。

医联网下医源性风险偏差感知扩散模型及干预

首先,本研究从多重网络理论视角出发,构建医联网环境下医源性风险偏差感知多渠道扩散模型.该模型考虑了医联网环境下不同信息渠道之间医源性风险偏差感知扩散的交互影响;其次,得出区分医联网下医源性风险偏差感知在公众中扩散开来与否的阈值;最后,利用实际数据对所建立的理论模型进行参数估计及案例分析.研究结论表明:1)与仅进行正确认知的宣传推广相比,在正确认知宣传的同时进行认知纠偏,医联网下医源性风险偏差感知的扩散效率将降低的更加明显;2)与仅对一种或两种渠道中医源性风险偏差感知的扩散进行深度管控相比,如果同时对所有渠道中医源性风险偏差感知的扩散均进行适度干预,医源性风险偏差感知的扩散效率将更显著地降低.

基于多层网络控制的个体化癌症驱动基因识别方法

目的识别癌症驱动基因,特别是罕见或个体特异性癌症驱动基因,对精准肿瘤学至关重要。考虑到肿瘤间的高度异质性,最近有一些方法尝试在个体水平上识别癌症驱动基因。然而,这些方法大多是将多组学数据整合到单一生物分子网络(如基因调控网络或蛋白质相互作用网络)中来识别癌症驱动基因,容易忽略不同网络所特有的重要相互作用信息。为了整合不同生物分子网络的相互作用数据,促进癌症驱动基因识别,迫切需要发展一种多层网络方法。方法本文提出了一种多层网络控制方法(PDGMN),利用多层网络识别个体化癌症驱动基因。首先,利用基因表达数据构建针对个体病人的个体化多层网络,其中包括蛋白质相互作用层和基因相互关联层。然后,整合突变数据,对个体化多层网络中的节点进行加权。最后,设计了一种加权最小顶点覆盖集识别算法,找到个体化多层网络中的最优驱动节点集,以提高个体化癌症驱动基因的识别效果。结果在三个TCGA癌症数据集上的实验结果表明,PDGMN在个体化驱动基因识别方面优于其他现有方法,并能有效识别个体病人的罕见癌症驱动基因。特别是,在不同生物分子网络上的实验结果表明,PDGMN能够捕获不同生物分子网络的独有特征,从而改进癌症驱动基因的识别结果。结论PDGMN能有效识别个体化癌症驱动基因,并从多层网络的视角,加深我们对癌症驱动基因识别的理解。本文所用的源代码和数据集可以从https://github.com/NWPU-903PR/PDGMN获取。

多层网络视角下全球石墨贸易竞争网络结构及其演化特征

相较于单一天然石墨产品贸易,立足于石墨产业链的综合视角深入分析不同石墨产品的跨产品贸易竞争格局,对于保障一国石墨全产业链的安全稳定发展具有重要意义。为了从产业链整体视角深入挖掘全球石墨跨国家跨产品贸易的复杂竞争格局,本文在已有研究的基础上,补充计算了不同石墨产品跨产品竞争强度,并利用多层网络模型构建了全球石墨贸易竞争网络,进一步分析了全球石墨贸易竞争网络的结构及其演化特征。研究结果表明,2000—2021年间全球石墨贸易竞争网络的规模持续增长,并且国家间的贸易竞争关系日益紧密。此外,尽管2000—2021年间不同石墨产品的贸易竞争网络存在稳定且显著的结构相似性,但是不同石墨产品贸易竞争网络的加权度相关性呈现下降趋势,这意味着不同石墨产品的贸易竞争格局可能会逐渐差异化。进一步的结果表明,石墨原材料贸易和石墨初级产品贸易的层间竞争呈现显著上升的趋势,这意味着各国不仅围绕同类石墨产品的进口存在竞争关系,而且这种竞争关系还存在沿产业链跨产品转移的可能性。本文为制定石墨贸易竞争策略、提高石墨产业国际竞争力提供了一定的参考信息。

AGCFN:基于图神经网络多层网络社团检测模型

基于图神经网络的多层网络社团检测方法面临以下两个挑战。一是如何有效利用多层网络的节点内容信息,二是如何有效利用多层网络的层间关系。因此,提出多层网络社团检测模型AGCFN(autoencoder-enhanced graph convolutional fusion network)。首先通过自编码器独立提取每个网络层的节点内容信息,通过传递算子将提取到的节点内容信息传递给图自编码器进行当前网络层节点内容信息与拓扑结构信息的融合,从而得到当前网络层每个节点的表示,这种方法充分利用了网络的节点内容信息与拓扑结构信息。对于得到的节点表示,通过模块度最大化模块和图解码器对其进行优化。其次,通过多层信息融合模块将每个网络层提取到的节点表示进行融合,得到每个节点的综合表示。最后,通过自训练机制训练模型并得到社团检测结果。与6个模型在三个数据集上进行对比,ACC与NMI评价指标有所提升,验证了AGCFN的有效性。

注意力去噪与复数LSTM的时变信道预测算法

随着无线通信技术的发展,高速场景下通信技术的研究也越来越广泛,其中获取到准确的信道状态信息对提升无线通信系统的性能具有重要的意义。针对正交频分复用系统在高速场景下,现有信道预测算法未考虑噪声影响及预测精度低的问题,提出了一种注意力去噪与复数卷积LSTM的时变信道预测算法。首先,设计了一种通道注意力信道去噪网络对信道状态信息进行去噪处理,降低了噪声对信道状态信息的影响。然后,提出了基于复数卷积层和长短期记忆网络的信道预测模型,对去噪后历史时刻的信道状态信息进行特征提取,并且对未来时刻的信道状态信息进行预测;改进后的LSTM预测模型增强了对信道时序特征的提取能力,提高了信道预测的精度。最后,结合Adam优化器对未来时刻信道状态信息进行预测输出。仿真结果表明:与对比算法相比,所提基于注意力去噪与复数卷积LSTM的时变信道预测算法对信道状态信息的预测精度更高,能够适用于高速移动场景下的时变信道预测。