The shortest path query problem in large scale time-varying transit network is NP-hard. Approximate search algorithm is not satisfactory, and the exact search algorithm is inefficient. In this paper, a time-varying mathematical model for transit network is formulated according to the time-varying and uncertainty characteristic of public transport network. The choice of the optimal path for transit network is decomposed into transfer times and transfer line query problem. A query algorithm for transfer times is proposed based on the line mapping network and an algorithm for both transfer site and travel distance is proposed based on site mapping network. Both of them are polynomial. Finally we use a numerical example to illustrate the solution process of the proposed method.

This paper presents an evaluation method of section importance based on the field theory in complex network of roads. The main differences with respect to previous evaluation method of field theory are as follows: Using the theory of gravity, gravity between the nodes in the definition to simulate the congestion queuing phenomenon; Sections of gravitational field node said not only associated with adjacent nodes, and other surrounding nodes. Based on this consideration, defines the gravitational field of the road node representation method; Using the gravitational field of the road node representation method to define evaluation function of importance Considering the evaluation function in the network congestion occurs will produce a large amount of 0 or tends to zero to evaluate the importance of these nodes in same, evaluation function parameters are introduced. The importance of the experimental results in Chengdu city road network show that the proposed road evaluation method on the dynamic response to the key network node congestion degree, and the parameters, the increase in value of time can very congestion in road network when adjust the value of the gravitational field of the node, improve the accuracy and precision of the assessment.

In this paper, we consider the impact of individuals' multi-strategy behavior—dynamic mixed strategy on the vaccination coverage and the final epidemic size under voluntary vaccination mechanism. In this dynamic mixed strategy, individuals can choose the imitation strategy or the benefit maximization strategy, at the same time, individual can switch between the two strategies by comparing their cost. We find that, compared with the pure imitation strategy, although the dynamic mixed strategy offers individuals more options to choose, this strategy is not conducive to control the spread of epidemic, instead of leading to a wider dissemination.

According to the seven regional division of China’s civil aviation air traffic control, flight networks of each air traffic control region are constructed and compared based on the flight data of National Flight Plan 2010—2013. Complex network analyzing method is employed to calculate the corresponding eigenvalues of the seven regions, proving the small-world and scale-free property of them. Survivability measurement based on degree priority and Matthew's recovery strategy analyses are conducted to each network, showing greatly improved network survivability after single-level network recovery. Research result indicates that flight network of each air traffic control region should be developed aiming at multi-hub and multi-level, helping strengthen aviation network invulnerability.

According to the characteristics of the pessimism infection of the depositors, we construct the pessimism infection model for depositors by means of population model in biology. The dynamic process of the contagion in time and space is simulated by the cellular automata model. Through the study of transmission rules and the dynamics behavior the causes of the bank runs will be reveled from the microscopic level of the pessimism contagion for depositors. It is significant to block and postpone the early withdrawal of depositors. At the same time, it can provide theoretical support for us to prevent the banks from the banking crisis, and at the same time it also can keep the bank system stable.

In this paper, the effect of noise intensity on the spatial public goods game behavior is analyzed. The results of simulation show that the noise plays an inhibitory role in the emergence of cooperative behavior, and the inhibitory effect will be more obvious when the multiplication factor is smaller. Another interesting finding is that: curve of the frequency of cooperation will present ladder phenomenon with the multiplication factor increase when the noise is relatively small, besides the phenomenon will be more evident when the noise intensity becomes smaller. Curve will be cut into several segments when the noise intensity is very small and each section corresponds to a unique space pattern. Finally, we explore the reason why the frequency of cooperation emergences segment and the theoretical analysis account for this phenomenon in detail.

In order to study the evolutionary mechanisms of spatial structure of urban logistics network, a weighted local world evolution model was proposed based on the agglomeration degree, considering the macro factors and the operating characteristics. By simulation analysis, the network features were studied, and the impact on the evolution which agglomeration degree parameters changed over time was also analyzed. The results reveal that the network’s degree distribution doesn’t obey the power law distribution, and it has higher clustering coefficient and lesser average path length, which shows a few hub nodes predominate in the urban logistics network and the network has strong anti-risk ability and quick response ability. The results also indicate that when the weight factors α,β,γ are changed over time, the evolution mechanism is more realistic, because it could overcome excessive influence of the node degree and reflect impact of all the related macro factors reasonably.

Recent literature has revealed that the classical rumor spreading models were severely short when descripting the complex activity patterns of online social network (OSN). In this paper, we proposed a new dynamic rumor spreading model (DRSIR) based on OSN. In this model, we introduced the time delay annihilation function of spreaders, the dynamic authority and resistance of nodes and the receiving reinforced signal mechanisms with the aim of filling the gap between theoretical and experimental results. We verified the effectiveness of the model by simulating rumor spreading on the real-world OSN which crawled from Sina microblog. To Identifying influential nodes in directed OSN, we used the DRSIR model to perform computer simulations by the initialization of every node to be the single spreader to examine the spreading influence of the nodes ranked by different centrality measures and find surprising results. Namely, the spreading capabilities of the nodes do not depend on their k-core index, which is obtained by successive pruning of nodes with degree, and instead, the outdegree and the closeness centrality of nodes seem to be the better topological descriptor to locate such influential individuals.

The generalized matrix projective synchronization of uncertain complex networks is investigated in this paper based on the Lyapunov stability theory, where each network may have non-identical topological structure and different dimensions. When parameters of nodes are uncertain, two different complex networks with time-varying delay coupling can realize GMPS by constructing an adaptive controller. In addition, for the unknown drive network and a given generalized matrix, the response network is constructed to realize GMPS. It can not only control the network to achieve the synchronization but also identify the unknown parameters to affirm the structure of the network. Finally, illustrative examples are presented to demonstrate the application of the theoretical results.

In order to study the general evolution law of mass emergency,a community advice and behavior evolutionary model is constructed, using the law of universal gravitation and kinetic theory on the basis of social physics.Simulation is conducted used the Netlog.Through adjusting the disposal power of the government, the time when to intervene and the authoritative of media and opinion leaders, influence , the time when to intervene to explore the impact of various factors,it is concluded that the government plays a main role in the evolution of mass incidents, the media and opinion leaders play a guiding role. Therefore, in the mass emergency, the government should select scientific behavior patterns actively, and use the media, opinion leaders, giving full play to the guiding role, to control the evolution of the mass emergency effectively.

In this paper, we propose a weight-driven evolution model of directed stock investment network, whose new nodes join in the network with the mechanism of doubly stochastic Poisson process. The model introduces the strength preferential attachment and dynamical evolution to mimic the weighted growing network. The calculation shows that power law distribution for both out- in strength and the evolution of nodes, which are all consistent with empirical evidence.

Existing methods have problems such as low accuracy or time consuming. Through empirical analysis of real data sets, we find that there is a strong correlation between future citations and annual citation of papers, i.e. the sustained attention; meanwhile, the future citations have apparent characteristics of time decay. Thus we put forward a method based on sustained attention decay to predict the future citations of papers, and then to find papers of potential importance with these results. Experimental results on two benchmark data sets show that the proposed method can predict precisely with lower time complexity.

An epidemic model with awareness among households is proposed to explore the impact of delayed global intervention and local awareness on early epidemic dynamics with the two levels of mixing structure. A number of explicit results are derived by focusing on different transition rates induced by delayed awareness perception in path integral of Markov chains. The different influences of global awareness and local awareness on early dynamics are assessed by introducing delays between the initial infection and the deployment of awareness. We show that the local awareness is less sensitive to the delay than global awareness when the contact number is considered.

In order to study social learning in complex social networks, a social learning model based on composite belief update strategy is proposed by considering the heterogeneity and complexity of the social individuals. At each time step, individuals in the model choose Bayesian update strategy or the update strategy based on their neighbors’ beliefs according to the strategy selection probability. The simulation results show that under some conditions such as the positive strategy selection probability, all the social individuals can achieve asymptotic learning. Furthermore, the learning speed is relative to the strategy selection probability, the larger the strategy selection probability is, the faster the learning speed will be.

With the general consideration of the impact of such factors as the consumers’ expectation, the retailer’s recovery service, regional differences on the expected salvage value of waste household appliance, this paper establishes a complex dynamic model based on the consumers’ utility maximization, and investigates the influence of the acceptance degree to network recovery channel on the recovery channel profits and the system stability. It is found that as the acceptance degree to network recovery channel increases, the profits will firstly decrease to a minimum and then increase, while the stability of the recovery system will gradually decrease. Furthermore, the paper analyzes the reason and gives the solution, providing practical guidance for the policy-making in household appliance recovery market with multiple recovery modes.