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
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
The Social Internet of Things(SIoT)integrates the Internet of Things(IoT)and social networks,taking into account the social attributes of objects and diversifying the relationship between humans and objects,which over...The Social Internet of Things(SIoT)integrates the Internet of Things(IoT)and social networks,taking into account the social attributes of objects and diversifying the relationship between humans and objects,which overcomes the limitations of the IoT’s focus on associations between objects.Artificial Intelligence(AI)technology is rapidly evolving.It is critical to build trustworthy and transparent systems,especially with system security issues coming to the surface.This paper emphasizes the social attributes of objects and uses hypergraphs to model the diverse entities and relationships in SIoT,aiming to build an SIoT hypergraph generation model to explore the complex interactions between entities in the context of intelligent SIoT.Current hypergraph generation models impose too many constraints and fail to capture more details of real hypernetworks.In contrast,this paper proposes a hypergraph generation model that evolves dynamically over time,where only the number of nodes is fixed.It combines node wandering with a forest fire model and uses two different methods to control the size of the hyperedges.As new nodes are added,the model can promptly reflect changes in entities and relationships within SIoT.Experimental results exhibit that our model can effectively replicate the topological structure of real-world hypernetworks.We also evaluate the vulnerability of the hypergraph under different attack strategies,which provides theoretical support for building a more robust intelligent SIoT hypergraph model and lays the foundation for building safer and more reliable systems in the future.展开更多
图为建模现实系统的内在交互提供了一种有效的方式,但却无法显示捕获的多个实体之间广泛存在的高阶异质性,超图则可以很好地突破低阶关系的限制。超网络的链路预测就是根据观测到的超图结构来预测未知的超链路,因其可以充分地刻画复杂...图为建模现实系统的内在交互提供了一种有效的方式,但却无法显示捕获的多个实体之间广泛存在的高阶异质性,超图则可以很好地突破低阶关系的限制。超网络的链路预测就是根据观测到的超图结构来预测未知的超链路,因其可以充分地刻画复杂系统的关联模式而成为网络科学中的热点问题。现有的方法通常针对整个拓扑结构设计推理模型,忽略了网络中隐含的聚集特性,导致预测的超链路类别不全面。针对上述问题,提出了基于超图谱聚类解析器的协调矩阵最小化(coordination matrix minimization based on hyper graph spectral clustering parser,SCL-CMM)模型的超网络链路预测方法。该方法将高阶超网络映射到具有一定语义的异质超图上,然后利用谱聚类解析器来提取超链路的结构特征,将原始超图重构为多个同质子图,进而在子图的观测空间而不是整个网络的邻接空间推断潜在超链路的分布情况,还原完整的超网络结构。该方法联合学习超网络的结构特征与集聚属性来建模各个子图的高阶非线性行为,解决了异构超图链路预测类别单一、精度低的问题。在9个真实数据集上进行了大量的对比实验表明,该方法在AUC(area under curve)评分和召回率方面都显著优于现有方法。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
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The Social Internet of Things(SIoT)integrates the Internet of Things(IoT)and social networks,taking into account the social attributes of objects and diversifying the relationship between humans and objects,which overcomes the limitations of the IoT’s focus on associations between objects.Artificial Intelligence(AI)technology is rapidly evolving.It is critical to build trustworthy and transparent systems,especially with system security issues coming to the surface.This paper emphasizes the social attributes of objects and uses hypergraphs to model the diverse entities and relationships in SIoT,aiming to build an SIoT hypergraph generation model to explore the complex interactions between entities in the context of intelligent SIoT.Current hypergraph generation models impose too many constraints and fail to capture more details of real hypernetworks.In contrast,this paper proposes a hypergraph generation model that evolves dynamically over time,where only the number of nodes is fixed.It combines node wandering with a forest fire model and uses two different methods to control the size of the hyperedges.As new nodes are added,the model can promptly reflect changes in entities and relationships within SIoT.Experimental results exhibit that our model can effectively replicate the topological structure of real-world hypernetworks.We also evaluate the vulnerability of the hypergraph under different attack strategies,which provides theoretical support for building a more robust intelligent SIoT hypergraph model and lays the foundation for building safer and more reliable systems in the future.
文摘图为建模现实系统的内在交互提供了一种有效的方式,但却无法显示捕获的多个实体之间广泛存在的高阶异质性,超图则可以很好地突破低阶关系的限制。超网络的链路预测就是根据观测到的超图结构来预测未知的超链路,因其可以充分地刻画复杂系统的关联模式而成为网络科学中的热点问题。现有的方法通常针对整个拓扑结构设计推理模型,忽略了网络中隐含的聚集特性,导致预测的超链路类别不全面。针对上述问题,提出了基于超图谱聚类解析器的协调矩阵最小化(coordination matrix minimization based on hyper graph spectral clustering parser,SCL-CMM)模型的超网络链路预测方法。该方法将高阶超网络映射到具有一定语义的异质超图上,然后利用谱聚类解析器来提取超链路的结构特征,将原始超图重构为多个同质子图,进而在子图的观测空间而不是整个网络的邻接空间推断潜在超链路的分布情况,还原完整的超网络结构。该方法联合学习超网络的结构特征与集聚属性来建模各个子图的高阶非线性行为,解决了异构超图链路预测类别单一、精度低的问题。在9个真实数据集上进行了大量的对比实验表明,该方法在AUC(area under curve)评分和召回率方面都显著优于现有方法。
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.
