Research on China’s Risk of Housing Price Contagion Based on Multilayer Networks

The major issue in the evolution of housing prices is risk of housing price contagion. To model this issue, we constructed housing multilayer networks using transfer entropy, generalized variance decomposition, directed minimum spanning trees, and directed planar maximally filtered graph methods, as well as China’s comprehensive indices of housing price and urban real housing prices from 2012 to 2021. The results of our housing multilayer networks show that the topological indices (degree, PageRank, eigenvector, etc.) of new first-tier cities (Tianjin, Qingdao, and Shenyang) rank higher than those of conventional first-tier cities (Beijing, Shanghai, Guangzhou, and Shenzheng).     

Systemic Risk Analysis of Multi-Layer Financial Network System Based on Multiple Interconnections between Banks,Firms, and Assets

Global financial systems are increasingly interconnected, and risks can spread more easily, potentially causing systemic risks. Research on systemic risk based on multi-layer financial networks is relatively scarce, and studies usually focus on only one type of risk. This paper develops a model of the multi-layer financial network system based on three types of links: firm-bank credit, asset-bank portfolio, and interbank lending, which simulates systemic risk under three risk sources: firm credit default, asset depreciation, and bank bankruptcy. The impact of the multi-layer financial network structure, default risk threshold, and bank asset allocation strategy is further explored. It has been shown that the larger the risk shock, the greater the systemic risk under different risk sources, and the risk propagation cycle tends to rise and then decline. As centralized nodes in the multi-layer financial network system, bank nodes may play both blocking and propagation roles under different risk sources. Furthermore, the multi-layer financial network system is most susceptible to bank bankruptcy risk, followed by firm credit default risk. Further research indicates that increasing the average degree of firms in the bank–firm credit network, the density of the bank-asset portfolio network, and the bank capital adequacy ratio all contribute to reducing systemic risk under the three risk sources. Additionally, the more assets a bank holds in a single market, the more vulnerable it is to the risks associated with that market.     

Properties of Bottleneck on Complex Networks

The traffic bottleneck plays a key role in most of the natural and artificial network. Here we present a simply model for bottleneck dynamical characteristics consideration the reliability on the complex network by taking into account the network topology characteristics and system size. We find that there is a critical rate of flow generation below which the network traffic is free but above which traffic congestion occurs. Also, it is found that random networks have larger critical flow generating rate than scale free ones. Analytical results may be practically useful for designing networks, especially for the urban traffic network.     

Research on Financial Systemic Risk in ASEAN Region

The research of financial systemic risk is an important issue, however the research on the financial systemic risk in ASEAN region lacks. This paper uses the minimum density method to calculate the interbank network of ASEAN countries and uses the node centrality to judge the systemically important banks of various countries. Then the DebtRank algorithm is constructed to calculate the systemic risk value based on the interbank network. By comparing the systemic risk values obtained through the initial impact on the system important banks and non-important banks, we find that the systemic risk tends to reach the peak in the case of the initial impact on the system important banks. Furthermore, it is found that countries with high intermediary centrality and closeness centrality have higher systemic risk. It suggests that the regulatory authorities should implement legal supervision, strict supervision, and comprehensive supervision for key risk areas and weak links.     

Research on Community Detection in Complex Networks Based on Internode Attraction

With the rapid development of computer technology, the research on complex networks has attracted more and more attention. At present, the research directions of cloud computing, big data, internet of vehicles, and distributed systems with very high attention are all based on complex networks. Community structure detection is a very important and meaningful research hotspot in complex networks. It is a difficult task to quickly and accurately divide the community structure and run it on large-scale networks. In this paper, we put forward a new community detection approach based on internode attraction, named IACD. This algorithm starts from the perspective of the important nodes of the complex network and refers to the gravitational relationship between two objects in physics to represent the forces between nodes in the network dataset, and then perform community detection. Through experiments on a large number of real-world datasets and synthetic networks, it is shown that the IACD algorithm can quickly and accurately divide the community structure, and it is superior to some classic algorithms and recently proposed algorithms.     

Discontinuity and Complex Dynamics of Neural Networks

We focus on the discontinuity of a neural network model with diluted and clipped synaptic connections (±l only). The exact evolution rule of the average firing rate becomes a discontinuous piece-wise nonlinear map when very simple functions of dynamical threshold are introduced into the network. Complex dynamics is observed.     

基于动力中继的小世界复杂电机网络混沌同步控制

利用链式结构中间节点参数不匹配能降低两个非直接相连外部节点的同步耦合强度临界值,促进两节点同步的特性,对一个双向耦合的小世界电机网络进行同步控制.首先从外部增加参数不匹配的中继节点,通过动力中继降低整个网络的同步耦合强度阈值,从而促进整个电机网络的同步,然后分析动力中继如何作用于网络,最后用数值仿真验证该方法的有效性.     

Finite-Time Generalized Outer Synchronization Between Two Different Complex Networks

This paper investigates the finite-time generalized outer synchronization between two complex dynamical networks with different dynamical behaviors. The two networks can be undirected or directed, and they may also contain isolated nodes and clusters. By using suitable controllers, sufficient conditions for finite-time generalized outer synchronization are derived based on the finite-time stability theory. Finally, numerical examples are examined to illustrate the effectiveness of the analytical results. The effect of control parameters on the synchronization time is also numerically demonstrated.     

New Approach to Cluster Synchronization in Complex Dynamical Networks

In this paper, a distributed control strategy is proposed to make a complex dynamical network achieve cluster synchronization, which means that nodes in the same group achieve the same synchronization state, while nodes in different groups achieve different synchronization states. The local and global stability of the cluster synchronization state are analyzed. Moreover, simulation results verify the effectiveness of the new approach.     

Finite-Time Generalized Outer Synchronization Between Two Different Complex Networks

This paper investigates the finite-time generalized outer synchronization between two complex dynamical networks with different dynamical behaviors. The two networks can be undirected or directed, and they may also contain isolated nodes and clusters. By using suitable controllers, sufficient conditions for finite-time generalized outer synchronization are derived based on the finite-time stability theory. Finally, numerical examples are examined to illustrate the effectiveness of the analytical results. The effect of control parameters on the synchronization time is also numerically demonstrated.     

Tsallis Mapping in Growing Complex Networks with Fitness

We introduce Tsallis mapping in Bianconi-Barab'asi (B-B) fitness model of growing networks.This mapping addresses the dynamical behavior of the fitness model within the framework of nonextensive statistics mechanics,which is characterized by a dimensionless nonextensivity parameter q.It is found that this new phenomenological parameter plays an important role in the evolution of networks:the underlying evolving networks may undergo a different phases depending on the q exponents,comparing to the original B-B fitness model,and the corresponding critical transition temperature T C is also identified.     

关联复杂网络建模及辨识研究

运用异质耦合拆分方法和驱动-响应模型,提出关联复杂网络节点参数和拓扑结构的辨识方法.首先,研究异质关联复杂网络建模方法,进而依据网络耦合性质不同,拆分构造了两类异质关联复杂网络.然后运用驱动-响应模型、LaSalle不变原理和Gram矩阵,设计节点系统参数和拓扑参数的自适应辨识观测器.所提的观测器能在线获取网络的节点参数、不同耦合性质的拓扑参数.最后,通过数值仿真验证所提方法的有效性.     

Research of Software Defect Prediction Model Based on Complex Network and Graph Neural Network

The goal of software defect prediction is to make predictions by mining the historical data using models. Current software defect prediction models mainly focus on the code features of software modules. However, they ignore the connection between software modules. This paper proposed a software defect prediction framework based on graph neural network from a complex network perspective. Firstly, we consider the software as a graph, where nodes represent the classes, and edges represent the dependencies between the classes. Then, we divide the graph into multiple subgraphs using the community detection algorithm. Thirdly, the representation vectors of the nodes are learned through the improved graph neural network model. Lastly, we use the representation vector of node to classify the software defects. The proposed model is tested on the PROMISE dataset, using two graph convolution methods, based on the spectral domain and spatial domain in the graph neural network. The investigation indicated that both convolution methods showed an improvement in various metrics, such as accuracy, F-measure, and MCC (Matthews correlation coefficient) by 86.6%, 85.8%, and 73.5%, and 87.5%, 85.9%, and 75.5%, respectively. The average improvement of various metrics was noted as 9.0%, 10.5%, and 17.5%, and 6.3%, 7.0%, and 12.1%, respectively, compared with the benchmark models.     

基于复杂网络的时间序列双变量联动波动

选择伦敦金与Au9999下午收盘价格作为样本数据研究时间序列双变量之间的联动波动规律.依据粗粒化方法,将伦敦金与Au9999价格的联动波动状态转化为由5个{P,N,M}字符组成的字符串,每个字符串代表5天的价格联动波动模态.将模态作为节点,模态之间的转化为边,构建价格联动波动复杂网络.运用复杂网络理论对时间序列双变量联动波动模态的统计、变化规律和演化机制进行分析.结果表明:时间序列双变量联动波动模态分布具有幂律性、群簇性和周期性,其联动波动模态主要通过少数几种模态进行转换与演化.本方法不仅可以研究不同类型时间序列双变量联动波动,同时可为多变量联动波动研究提供思路.     

Identifying Important Nodes in Complex Networks Based on Node Propagation Entropy

In recent years, the identification of the essential nodes in complex networks has attracted significant attention because of their theoretical and practical significance in many applications, such as preventing and controlling epidemic diseases and discovering essential proteins. Several importance measures have been proposed from diverse perspectives to identify crucial nodes more accurately. In this paper, we propose a novel importance metric called node propagation entropy, which uses a combination of the clustering coefficients of nodes and the influence of the first- and second-order neighbor numbers on node importance to identify essential nodes from an entropy perspective while considering the local and global information of the network. Furthermore, the susceptible–infected–removed and susceptible–infected–removed–susceptible epidemic models along with the Kendall coefficient are used to reveal the relevant correlations among the various importance measures. The results of experiments conducted on several real networks from different domains show that the proposed metric is more accurate and stable in identifying significant nodes than many existing techniques, including degree centrality, betweenness centrality, closeness centrality, eigenvector centrality, and H-index.     

Simplicial Persistence of Financial Markets: Filtering,Generative Processes and Structural Risk

We introduce simplicial persistence, a measure of time evolution of motifs in networks obtained from correlation filtering. We observe long memory in the evolution of structures, with a two power law decay regimes in the number of persistent simplicial complexes. Null models of the underlying time series are tested to investigate properties of the generative process and its evolutional constraints. Networks are generated with both a topological embedding network filtering technique called TMFG and by thresholding, showing that the TMFG method identifies high order structures throughout the market sample, where thresholding methods fail. The decay exponents of these long memory processes are used to characterise financial markets based on their efficiency and liquidity. We find that more liquid markets tend to have a slower persistence decay. This appears to be in contrast with the common understanding that efficient markets are more random. We argue that they are indeed less predictable for what concerns the dynamics of each single variable but they are more predictable for what concerns the collective evolution of the variables. This could imply higher fragility to systemic shocks.     

Outer Synchronization of Complex Networks by Impulse

This paper investigates outer synchronization of complex networks, especially, outer complete synchronization and outer anti-synchronization between the driving network and the response network. Employing the impulsive control method which is uncontinuous, simple, efficient, low-cost and easy to implement in practical applications, we obtain some sufficient conditions of outer complete synchronization and outer anti-synchronization between two complex networks. Numerical simulations demonstrate the effectiveness of the proposed impulsive control scheme.     

基于元胞自动机的复杂信息系统安全风险传播研究

基于元胞自动机建立复杂信息系统安全风险传播模型, 研究复杂信息系统安全风险在最近邻耦合网络、 随机网络,　Watts-Strogatz 小世界网络和Barabasi-Albert无标度网络 四种网络拓扑下的传播问题. 通过研究安全风险传播模型在四种网络拓扑下安全风险的传播阈值, 与现有的传播阈值研究成果进行比较, 验证模型的正确性, 并分析验证网络拓扑结构中度分布的异质化程度越高传播阈值越小的结论. 通过对安全风险的传播演化趋势进行研究, 分析验证网络度分布的异质化程度越高、安全风险影响范围越小、传播速度越快的结论, 并指出度分布的异质化程度越高、模型后期的免疫机制对控制安全风险传播的效果越缓慢. 通过对安全风险在传播最早期就趋于消亡的情况进行研究, 分析得出安全风险在传播之初就趋于消亡的消亡率与传播率之间呈现近似负指数的关系, 并且初期的感染源越多安全风险的消亡率越低. 分析了影响复杂信息系统安全风险传播的关键要素, 对复杂信息系统中安全风险传播的控制具有指导作用. 关键词： 复杂信息系统 复杂网络 安全风险传播 元胞自动机     

Fixed-Time Outer Synchronization of Complex Networks with Noise Coupling

In this paper,the fixed-time outer synchronization of complex networks with noise coupling is investigated.Based on the theory of fixed-time stability and matrix inequalities,sufficient conditions for fixed-time outer synchronization are established and the estimation of the upper bound of the setting time is obtained.The result shows that the setting time can be adjusted to a desired value regardless of the initial states.Numerical simulations are performed to verify the effectiveness of the theoretical results.The effects of control parameters and the density of controlled nodes on the converging time are studied.