To explore and study the regular pattern of the cooperation evolution of enterprises, this paper introduces the concept of two-stage power law distribution on the basis of studying the degree distribution index of realistic complex networks. And constructs an evolution model according two-stage power-law distribution combined with fitness and noise fluctuation under complex network. Then using the numericalsimulation to analyze the model, the simulation results show that the fitness of normal distribution is the dominant factor in the generation of two-stage power law distribution. Noise fluctuation has little effect on enterprise value, but it can affect power exponents. Finally, this paper takes the supply chain network of China's automobile manufacturing industry as an example.The power exponents γ₁ and γ₂of the two-stage power law distribution of the network are 0.59 and 3.4 separately, which is in good agreement with the evolution of the supply chain network of China's automobile manufacturing industry.Also example verifies the validity of proposed model, and expounds the mechanism of cooperative evolution of supply chain network enterprises.

The deep integration of electricity network and information networks can promote communication between networks, but also brings the risk of large-scale transmission of failures. Based on the grid structure and load characteristics, combined with the dispatching function of information network, we construct a "power-information interdependence network" model. Three types of node attack methods are applied to attack a single node of the power grid where the attacked node is the highest load node, the lowest load node or the highest capacity proportion node. The cascade effects are compared with single-layer power grid. Study shows that the robustness of the power information interdependent network is weaker than that of the single-layer power grid under the highest load node attack when the initial load is small. The robustness of the two kinds of networks approaches each other when the initial load is large. The difference in robustness of the two kinds of networks is not obvious at the lowest load node and highest capacity proportion node attack. On the interdependent network, the cascading failure caused by the highest load node attack is the most difficult to eliminate completely.

This paper compares the two basic assumptions of individual behavior in the East and the West, introduces altruistic social preferences into the utility function, and explores how to promote the altruistic behavior and the supply of the quantity of altruism in the society by stimulating mechanism design. At the same time, individual benefits and society Maximization of overall welfare. The social groups were divided into heterogeneous individuals according to the degree of altruism, and the effects of different incentive mechanisms on heterogeneous individuals were analyzed. Finally, the effects of altruistic proportion on the total altruistic supply were analyzed, and the effects of the system design were analyzed.

The rapid development of network science has had a profound impact on the disciplines of physics, computers and management. However, the current development trend of the latest network science topics in China has been lack of intuitive analysis. This paper takes the summary of the 13th National Network Science Conference in 2017 as the research object, and analyzes the research trend of the most representative complex network conferences in network science from the perspective of topic extraction and clustering based on text mining. The research trend can reflect the latest research situation in the field of domestic network science to a certain extent. Firstly, the text information of the conference summary is preprocessed, and the text is jieba word segmentation through the self-built dictionary and the stop vocabulary. Then use the LDA topic model to identify the topic distribution of the abstract, and perform based on the JS distance between the abstracts to get 10 types of conference topics. This paper expands the application scope of the topic model in the research situation and research hotspots of academic conferences, enriches the ideas of academic conference topic mining and research hotspot analysis, and can provide reference for other academic conferences to quickly explore research situation. At the same time, it proposes a kind of combination of topic model and social network analysis, this paper explores the research methods of institutional associations, and uses the similarity of institutional research topics as reference indicators to provide reference for institutions to find suitable research cooperation units.

This paper builds two evolutionary game models of E-commerce platform and social media platform, consumers and social media platform to investigate the control strategies of false Word-of-Mouth information in social media at micro level, and the conditions promoting the strategies of participators to ideal stable states are given by model solving. The research results show that the control of false Word-of-Mouth information is closely related with the control power of social media platform, the probability of E-commerce platform assisting control, the mistake probability of false Word-of-Mouth information for consumers, etc. And cooperative governance among E-commerce platform, social media platform and consumers is the key to solve the problem of false Word-of-Mouth information in social media. We should reasonably control the short-term benefits and governance of social media, advocate its benign competition. And we should strengthen the positive direction of public opinion, reduce the cost of false Word-of-Mouth identification of E-commerce platforms, urge them to help governance. And improving consumer information literacy, inspiring the incentive mechanism, and arousing them to participate in governance.

The fractal property is considered as the third fundamental topology features of complex networks. The fractal dimension is the most important measure to characterize the fractal property of complex networks, and the volume dimension is widely used to investigate the fractal property of unweighted networks. In this paper, motivated by the idea of volume dimension of unweighted networks, a new volume dimension measure based on the node strength of weighted networks is proposed. We first apply the proposed method to study the fractal property of two families of weighted fractal networks with regular fractal structures:“Sierpinski” weighted fractal networks and “Cantor dust” weighted fractal networks. The result shows that the numerical fractal dimensions obtained by our method are very close to the theoretical similarity dimension of the network. Then, we use the proposed method to study the fractal property of three more representative real-world weighted networks and the results demonstrate that the proposed method is also effective for the fractal scaling analysis of real-world weighted complex networks.

To study the topological properties and optimization strategies of hazardous materials road transportation network (HMRTN), the HMRTN of Dalian city was used as a case to investigate the topological properties by establishing an undirected, weighted and connected HMRTN model through primal approach based on complex network theory. The results of eigenvalues of average node degree, node distribution, characteristic path length and average clustering coefficient etc. show that: 1) the degree distribution is extremely uneven, and the cumulative node degree is subjected to exponential distribution, showing the network characteristics of random network; 2) the HMRTN has small characteristic path length and large network clustering coefficient, showing the network characteristics of small-world network; 3) by reducing the network average road length in the similar conditions (the size of network, the characteristic path length, etc.), expanding the size of network and taking a planning method with sparse and dense network etc. are all good for improving the network characteristics of small-world network. These findings provide a new reference to optimize and enhance the HMRTN and its network planning.

As to the practical complex logistics distribution situation that the distribution task cannot be completed in time, which brings tardiness risk and loss for company, the routing optimization problem in the fourth party logistics with consideration of tardiness risk is studied. A mathematical model minimizing tardiness risk and taking the distribution costs as constraint is set up, using Value-at-Risk to measure time risk. A deletion algorithm embedded harmony search is proposed considering nonlinear and NP-Hard characteristic of the problem. The effectiveness of model and algorithm is verified through solving the different scales of cases.

In order to improve the effectiveness of portfolio selection, we firstly construct the statistical measures of fractal expectation and fractal variance, and give the algorithm of two fractal statistical measures. Secondly, a fractal portfolio selection model is built based on the fractal statistical measures and an analytical solution for fractal portfolio selection model is calculated. Lastly, drawing the sample of all industrial indexes from Shanghai Stock Exchange, we verify the feasibility of constructing portfolio selection model using two fractal statistical measures. The empirical results demonstrate that fractal statistical measures make up the defect of the non-fractal statistical measure’s disability to measure the return and risks of stocks accurately, and the fractal portfolio model is more effective in diversifying risks while ensuring returns.

In order to enhance the effective protection of color images, the multi-steady characteristics of a four-dimensional magnetron memristive model is analyzed by Lyapunov exponent spectrum and bifurcation diagrams. The color image encryption based on this system is implemented using Python programming. The analysis of the pixel histogram, the correlation between adjacent pixels, the anti-attack capability and the key sensitivity shows that the encryption performance of the chaotic state of the memristive system is superior to its periodic state. The analysis of the characteristic provides a method and support for selecting the appropriate key.

In this paper, the optimally weighted stochastic pooling network is investigated for the theoretical and experimental analyses of the random parameter estimation. The stochastic pooling network is first optimized by the random noise components, and then improved by the linear optimum weight coefficients. The theoretical expressions of the optimum weight vector and the mean square error of the stochastic pooling network with an arbitrary number of nodes are deduced. In practice, since the statistical information of parameter and background noise is often unknown, the approximation estimation algorithms of the optimum weight vector and the mean square error are presented and based on the observations. Theoretical and experimental results both verify the optimization ability of random noise, and show the outstanding estimation performance of the optimally weighted stochastic pooling network.