To explore a new thinking for network vulnerability research, and improve the practicality of spatial vulnerability model, this paper analyzes the relationship between spatial vulnerability and four traditional network properties, average shortest path, average betweenness, connectivity and amount of impacted shortest paths, and conducts a case study on Beijing′s subway network to verify this relationship. The results show that, when the global impact of spatial hazards is concerned in network vulnerability research, the reasonable combination of the new spatial vulnerability model and traditional network properties can deliver an effective approach.

We research the effect of information strength, inoculation ratio and individual information transmission rate on disease outbreaks in small-word network. The study finds that, the effect of information strength on the outbreak probability and scale of infectious disease occur reversal with the strength of small world weakening gradually. In the process of information transmission rate increasing, the outbreak probability and size of infectious disease outbreak show rising tendency.

We study the emergence of cooperation with self-organized criticality on a one-dimensional lattice by connecting Snowdrift Game and Bak-Sneppen (BS) model. We first calculate the mean cooperation probability of the system by Monte-Carlo simulation and the results show that there is a high level cooperation in the steady state,which is possible because the BS mechanism builds dynamical correlation between the least fit sites. Besides, we also measure the distribution of avalanche size and the distance between successive minimum fitness sites, which are well fit by a power law approximately. The power law distribution we measured shows that the system has reached a critical state. In the critical state the agents are correlated at all scales which closely connected with the high level cooperation in the system

Abstract: To reveal behaviors of population migration driven by interests, a generalized potential for a region is suggested and defined by income per capita, public service resources per capita and average age of the people within it.The dynamic rules are: when the generalized potential for a given region is higher than the average over all of the regions,some people would like to migrate out of this region;the regions that their generalized potentials are of lower than the average will become the migration destinations,immigration regions,for the upcoming migrating people;the allocation of such upcoming migrating people to the immigration regions is determined by difference of the generalized potentials between the given region and the corresponding immigration regions.The calculated results show some regulations that govern the migrating process: the number of migration people in a region,neither immigration or emigration, decays generally in an exponential way with time,which leads to the amount of people, income per capita and public service resources per capita in the emigration/immigration region decreases /increases in an exponential/“anti-exponential” way, and finally reach a relative steady state at which the mentioned three key factors match each other. The variation of information entropy and the evolution of pattern-formation for the regions indicate that the system evolves towards order in the progress of polarization of population distribution.

This paper experimented with five virtual networks, such as the Air network of US, the Coauthorship network of Scientists, the Neural network of the nematode C, etc. and quantified the relationship between the network structure features and the link prediction algorithms by the experiment’s data. The network structure features could be measured by assortativity coefficient, clustering coefficient, etc. and the link prediction algorithms could be divided into local-information based and global-information based. After analyzed the data, we found that if the value of network’s assortativity coefficient is positive and the value of network’s clustering coefficient is greater than the threshold which is about 0.1, the local-information based would be the better choice, otherwise the global-information based would be better. And the clustering coefficient and the network efficiency is proportional to the result of link prediction algorithms based local information and is reverse proportional to the result of algorithms based global information. These conclusions provide quantitative basis for selecting the right algorithm.

Vulnerable nodes are very important to structural robustness of distribution networks, in order to assess node vulnerability of distribution networks, a method of sorting the vulnerability of distribution network nodes is proposed. Firstly, build the weighted complex network model of distribution network. Secondly, set weights of indexes such as degree, betweenness, agglomeration and closeness to assess importance of each index in describing the vulnerability of nodes, in the perspective of both subjective and objective way. Finally, put forward a method to sort vulnerability of distribution network nodes based on technique for order preference by similarity to ideal solution (TOPSIS) and grey correlation analysis. Test on several typical medium voltage di-stribution networks verifies the feasibility of the method. Analysis of the IEEE123 distribution network shows that multiple index comprehensive evaluation is more reasonable in distribution network than single index eva-luation.

The method of growth and preferential attachment applied by the classic BA scale-free network model to deal with connections between nodes of network will result in unlimited connections and other defects. This paper improves the method of connections of BA network model by introducing the maximum number of connections, having a sub-linear growth in the number of connections of new nodes and using Logistic function. Then a new network model named BE with a bimodal degree distribution is obtained. Its several properties are also provided. This model may be applied to explain the socio-economic polarization in the real world well. Moreover, the shifting and zooming of the peak may be achieved by adjusting its parameters. BE network model will be degenerated to BA network model in the limiting case.

Labeled LDA can mine words’ probabilities under a given topic, however, it can’t analyze the association relationships among these topic words. Although the correlation between word pairs can be calculated by utilizing PMI (Pointwise Mutual Information), their relationship to the given topic is lost. Motivated by the operation of counting word pairs in a fixed window used in PMI, this paper proposes a topic model called PL-LDA (Pointwise Labeled LDA), which can compute the joint probabilities between word pairs under a given topic. Experimental results on aviation safety reports show that this model achieves results with good interpretability. Based on the results of PL-LDA, this paper constructs a topic text network, which provides rich and effective information for analyzers including reflecting the distribution of topic words and displaying the complex relationships among them.

The efficiency of semantic reasoning can be improved through constructing the semantic ontology reasonably and reducing the redundant information in the process of reasoning. It is a feasible method to reveal the reason of the redundant information in the process of reasoning through analyzing the dynamic changes of an ontology structure and the important role of nodes in an ontology. Taking AGROVOC ontology network as an example, this paper provides qualitative analyses based on the reasoning mechanism of semantic web for understanding the redundant information. Meanwhile, some quantitative measurements from the perspective of complex network are provided in order to identify the core concepts in a semantic web, and further to solve the problem of redundant information. Experimental results show that the reasoning of semantic web and the rationality of ontology construction can be quantitatively analyzed from the perspective of complex network, which provides a new measurement to optimize the design of ontology and improve the efficiency of reasoning in the semantic web.

Daily data collected from Shanghai 50 Index constituent stocks from January 4, 2005 to December 31, 2008 is divided into three stages: bear I, bull and bear II. We study dynamic topology of stock correlation networks in each stage by using the minimal spannin tree (MST), hierarchical tree (HT) and main network property measures. The results show that: Industrial clustering exists and becomes more and more obvious in stock market; Manufacturing industry turns into the absolute center in bull market, which lasts until bear II market; Internal stocks of finance & insurance industry and steelmaking industry always maintain a high correlation, and the stocks of parent company and subsidiary and the stocks of the cross holdings companies are also close to each other; In addition, the main network property measures reveal that the structure of the stock market’s correlation network is closer but worse in bull market than in bear markets.

We build an interbank network model based on risk-averse behaviors. On a heterogeneous network structure, we explore the relationship between risk-averse behaviors of banks and systemic risk contagion, specifically, liquidity hoarding, fire sales behaviors and the composition of risk-averse behaviors. The simulation results show that liquidity hoarding behaviors mitigate systemic risk contagion at early stage, fire sales behaviors have little effect on mitigating systemic risk contagion and the composition of risk-averse behaviors exacerbate the systemic risk contagion. Heterogeneous network is more robust than the homogenous network if risk-averse behaviors exist, otherwise the homogeneous network is more stable. Furthermore, bank asset heterogeneity has no significant effect on systemic risk contagion.

We consider an SIRS epidemic model with a general direct immunization rate on networks. By constructing suitable Lyapunov functions, we find that the dynamical behvaior of the model is completely determined by the epidemic threshold λ_c. When λ≤λ_c, the disease-free equilibrium is globally asymptotically stable; when λ>λ_c, the endemic equilibrium is globally asymptotically stable. In addition, we propose a uniform direct immunization and a targeted direct immunization. The results show that under the same average immunization rate s there exists a critical immunization-lost rate δ_c so that the epidemic threshold of the targeted direct immunization is smaller (larger) than that of the uniform direct immunization if δ<δ_c(δ>δ_c).

To deal with the contradiction between the supply of infrastructures and the demand of multi-velocity traffic flow in a large scale evacuation, we present an algorithm based on time-extended network. It can be applied to making organized and staged evacuation plans. First, evacuees are grouped according to their locations and velocities. Then, individual starting time and route are allocated to each group in order to avoid any traffic conflict and ensure the efficiency of evacuation through recording time availability of each road segment. Following this plan, each group can move towards to the safe exit at their own velocity. Experiments demonstrate that, with this algorithm, the total evacuation time is close to the theoretical shortest evacuation time. And the larger the evacuation scale is, the better the algorithm performs.

With certain connection randomness and coupling strength, the permanent magnet synchronous motors in Newman-Watts small-world (NWSW) networks fall into chaotic motion and threaten the secure and stable operation of the drive system. To control the undesirable chaos in complex motor networks, an adaptive controller based on pinning method is first presented. And then, the stability of the controlled system is proved by Lyapunov stability theory and the threshold values of parameter conditions for the onset of stability are obtained. Finally, the simulation results show that the proposed control law is correct and effective. Our research results are helpful to maintain the secure operation of drive system.

A kind of generalized Hamiltonian systems with dissipative structure and external input is proposed. By configuring its structure matrix and external input, a simpler three-dimensional dynamical system with only one fixed point is designed to illustrate the existence of chaos. Useful tools, including phase portrait, Poincaré section, Lyapunov exponents, bifurcation diagram and power spectrum, are used for detecting chaotic behavior of the proposed system with the enhancement of DC input. Compared with the known three-dimensional chaotic systems, the proposed system has the following two characteristics: Its dissipativity is related to system state variables and its Lyapunov dimension is closer to 3. Finally, a circuit implementation of the new system is presented and the results recorded on an analogue oscilloscope further verify the existence of chaotic behavior.