The outbreak of COVID-19 in 2019 has made people pay more attention to infectious diseases.In order to reduce the risk of infection and prevent the spread of infectious diseases,it is crucial to strengthen individual ...The outbreak of COVID-19 in 2019 has made people pay more attention to infectious diseases.In order to reduce the risk of infection and prevent the spread of infectious diseases,it is crucial to strengthen individual immunization measures and to restrain the diffusion of negative information relevant to vaccines at the opportune moment.This study develops a three-layer coupling model within the framework of hypernetwork evolution,examining the interplay among negative information,immune behavior,and epidemic propagation.Firstly,the dynamic topology evolution process of hypernetwork includes node joining,aging out,hyperedge adding and reconnecting.The three-layer communication model accounts for the multifaceted influences exerted by official media channels,subjective psychological acceptance capabilities,self-identification abilities,and physical fitness levels.Each level of the decision-making process is described using the Heaviside step function.Secondly,the dynamics equations of each state and the prevalence threshold are derived using the microscopic Markov chain approach(MMCA).The results show that the epidemic threshold is affected by three transmission processes.Finally,through the simulation testing,it is possible to enhance the intensity of official clarification,improve individual self-identification ability and physical fitness,and thereby promote the overall physical enhancement of society.This,in turn,is beneficial in controlling false information,heightening vaccination coverage,and controlling the epidemic.展开更多
With the rapid development of the internet,the dissemination of public opinion in online social networks has become increasingly complex.Existing dissemination models rarely consider group phenomena and the simultaneo...With the rapid development of the internet,the dissemination of public opinion in online social networks has become increasingly complex.Existing dissemination models rarely consider group phenomena and the simultaneous spread of competing public opinion information in online social networks.This paper introduces the UHNPR information dissemination model to study the dynamic spread and interaction of positive and negative public opinion information in hypernetworks.To improve the accuracy of modeling of information dissemination,we revise the traditional assumptions of constant propagation and decay rates by redefining these rates based on factors that influence the spread of public opinion information.Subsequently,we validate the effectiveness of the UHNPR model using numerical simulations and analyze the impact of factors such as authority effect,user intimacy,information content and information timeliness on the spread of public opinion,providing corresponding suggestions for public opinion control.Our research results demonstrate that this model outperforms the SIR,SEIR and SEIDR models in describing public opinion propagation in real social networks.Compared with complex networks,information spreads faster and more extensively in hypernetworks.展开更多
Complex hypernetworks are ubiquitous in the real system. It is very important to investigate the evolution mecha- nisms. In this paper, we present a local-world evolving hypernetwork model by taking into account the h...Complex hypernetworks are ubiquitous in the real system. It is very important to investigate the evolution mecha- nisms. In this paper, we present a local-world evolving hypernetwork model by taking into account the hyperedge growth and local-world hyperedge preferential attachment mechanisms. At each time step, a newly added hyperedge encircles a new coming node and a number of nodes from a randomly selected local world. The number of the selected nodes from the local world obeys the uniform distribution and its mean value is m. The analytical and simulation results show that the hyperdegree approximately obeys the power-law form and the exponent of hyperdegree distribution is 7 = 2 ＋ 1/m. Furthermore, we numerically investigate the node degree, hyperedge degree, clustering coefficient, as well as the average distance, and find that the hypemetwork model shares the scale-flee and small-world properties, which shed some light for deeply understanding the evolution mechanism of the real systems.展开更多
The concepts of hypernetwork, composite hypergraph and its primary subhyper-graph are introduced, and the principle and algorithm of a new topological method-pri-mary subhypergraph method is presented for linear activ...The concepts of hypernetwork, composite hypergraph and its primary subhyper-graph are introduced, and the principle and algorithm of a new topological method-pri-mary subhypergraph method is presented for linear active hypernetwork analysis. The ex-pressions of the symbolic network functions generated by this method are very compactand contain no cancellation terms. Its computing time complexity is O(m^3c^2n_h+m_1u_G∑n_l);its order of magnitude is less than that in Refs. [1,2] by 2-3 orders.展开更多
Many phenomena in realistic complex systems can be explained by the synchronisation behavior of complex systems,such as cricket chirping in uni-son.The synchronisation behavior occurring on a hypernetwork can be used ...Many phenomena in realistic complex systems can be explained by the synchronisation behavior of complex systems,such as cricket chirping in uni-son.The synchronisation behavior occurring on a hypernetwork can be used to explain the swarming behavior occurring on a multivariate interacting system,such as the synchronised forwarding of group messages.There is a lack of results related to phase synchronization of hypernetwork in the existing studies on the synchronization behavior of hypernetworks.To address this problem,this paper investigates the node-based and hyperedge-based phase synchronisation of a scale-free hypernetwork using the Kuramoto model with the order parameter r as the synchronisation degree indicator.The comparative analysis reveals that the phase synchronisation of the scale-free hypernetwork is related to the uniformity k of the hypernetwork but not to the number of nodes and hyperedges,and the phase synchronisation based on hyperedges is more likely to occur than that based on nodes as the coupling strength increases.In addition,the degree of phase syn-chronisation of scale-free hypernetworks is related to the number of new_nodes of newly added nodes when the hyperedge grows during the construction of the hypernetwork,which shows that the smaller the new_nodes is,the better the degree of synchronisation of the hypernetwork is.展开更多
文摘The outbreak of COVID-19 in 2019 has made people pay more attention to infectious diseases.In order to reduce the risk of infection and prevent the spread of infectious diseases,it is crucial to strengthen individual immunization measures and to restrain the diffusion of negative information relevant to vaccines at the opportune moment.This study develops a three-layer coupling model within the framework of hypernetwork evolution,examining the interplay among negative information,immune behavior,and epidemic propagation.Firstly,the dynamic topology evolution process of hypernetwork includes node joining,aging out,hyperedge adding and reconnecting.The three-layer communication model accounts for the multifaceted influences exerted by official media channels,subjective psychological acceptance capabilities,self-identification abilities,and physical fitness levels.Each level of the decision-making process is described using the Heaviside step function.Secondly,the dynamics equations of each state and the prevalence threshold are derived using the microscopic Markov chain approach(MMCA).The results show that the epidemic threshold is affected by three transmission processes.Finally,through the simulation testing,it is possible to enhance the intensity of official clarification,improve individual self-identification ability and physical fitness,and thereby promote the overall physical enhancement of society.This,in turn,is beneficial in controlling false information,heightening vaccination coverage,and controlling the epidemic.
基金supported by Yunnan High-tech Industry Development Project(Grant No.201606)Yunnan Provincial Major Science and Technology Special Plan Projects(Grant Nos.202103AA080015 and 202002AD080001-5)+1 种基金Yunnan Basic Research Project(Grant No.202001AS070014)Talents and Platform Program of Science and Technology of Yunnan(Grant No.202105AC160018)。
文摘With the rapid development of the internet,the dissemination of public opinion in online social networks has become increasingly complex.Existing dissemination models rarely consider group phenomena and the simultaneous spread of competing public opinion information in online social networks.This paper introduces the UHNPR information dissemination model to study the dynamic spread and interaction of positive and negative public opinion information in hypernetworks.To improve the accuracy of modeling of information dissemination,we revise the traditional assumptions of constant propagation and decay rates by redefining these rates based on factors that influence the spread of public opinion information.Subsequently,we validate the effectiveness of the UHNPR model using numerical simulations and analyze the impact of factors such as authority effect,user intimacy,information content and information timeliness on the spread of public opinion,providing corresponding suggestions for public opinion control.Our research results demonstrate that this model outperforms the SIR,SEIR and SEIDR models in describing public opinion propagation in real social networks.Compared with complex networks,information spreads faster and more extensively in hypernetworks.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.71071098,91024026,and 71171136)supported by the Shanghai Rising-Star Program,China(Grant No.11QA1404500)the Leading Academic Discipline Project of Shanghai City,China(Grant No.XTKX2012)
文摘Complex hypernetworks are ubiquitous in the real system. It is very important to investigate the evolution mecha- nisms. In this paper, we present a local-world evolving hypernetwork model by taking into account the hyperedge growth and local-world hyperedge preferential attachment mechanisms. At each time step, a newly added hyperedge encircles a new coming node and a number of nodes from a randomly selected local world. The number of the selected nodes from the local world obeys the uniform distribution and its mean value is m. The analytical and simulation results show that the hyperdegree approximately obeys the power-law form and the exponent of hyperdegree distribution is 7 = 2 ＋ 1/m. Furthermore, we numerically investigate the node degree, hyperedge degree, clustering coefficient, as well as the average distance, and find that the hypemetwork model shares the scale-flee and small-world properties, which shed some light for deeply understanding the evolution mechanism of the real systems.
文摘The concepts of hypernetwork, composite hypergraph and its primary subhyper-graph are introduced, and the principle and algorithm of a new topological method-pri-mary subhypergraph method is presented for linear active hypernetwork analysis. The ex-pressions of the symbolic network functions generated by this method are very compactand contain no cancellation terms. Its computing time complexity is O(m^3c^2n_h+m_1u_G∑n_l);its order of magnitude is less than that in Refs. [1,2] by 2-3 orders.
文摘Many phenomena in realistic complex systems can be explained by the synchronisation behavior of complex systems,such as cricket chirping in uni-son.The synchronisation behavior occurring on a hypernetwork can be used to explain the swarming behavior occurring on a multivariate interacting system,such as the synchronised forwarding of group messages.There is a lack of results related to phase synchronization of hypernetwork in the existing studies on the synchronization behavior of hypernetworks.To address this problem,this paper investigates the node-based and hyperedge-based phase synchronisation of a scale-free hypernetwork using the Kuramoto model with the order parameter r as the synchronisation degree indicator.The comparative analysis reveals that the phase synchronisation of the scale-free hypernetwork is related to the uniformity k of the hypernetwork but not to the number of nodes and hyperedges,and the phase synchronisation based on hyperedges is more likely to occur than that based on nodes as the coupling strength increases.In addition,the degree of phase syn-chronisation of scale-free hypernetworks is related to the number of new_nodes of newly added nodes when the hyperedge grows during the construction of the hypernetwork,which shows that the smaller the new_nodes is,the better the degree of synchronisation of the hypernetwork is.
文摘对特定领域的技术机会进行挖掘与分析,可以为企业“从0到1”的原始创新提供新参考和新建议。本文提出了一种基于超链路预测的多元技术机会发现方法。首先,基于技术间多元共现关系构建技术关系超网络,利用IPC(international patent classification)的引用信息和文本信息生成节点特征向量;其次,将超链路预测模型Hyper-SAGNN(a self-attention based graph neural network for hypergraphs)扩展到技术关系超网络中,预测未来多个技术融合形成技术机会的可能性;最后,基于新颖性、中心性、跨领域性等特征构建度量指标,发现潜在的、有价值的多元技术机会。以智能问答技术领域为例,验证了本文方法的科学性和有效性,有效挖掘出高价值的三元技术机会和四元技术机会,为企业的技术战略布局与创新策略提供了决策支持。
