Mission of this research is to better capture the complexity of large-scale financial systems and overcome the insufficiency of equilibrium based neoclassical finance and behavioral finance in revealing the mechanism of financial crisis and the emergence of order. This study started from the dilemma of existing theories and the motivation of introducting complexity science. Then advances of multi-agent simulation and complex network analysis are summarized and discussed as two fundimental instruments of complexity science. Research trends of complexity science in financial research, are proposed as the result of the discussions. This study provides methodology reference and analytical tools for the theoretical research and practical applications of complex financial systems in the new era.

In order to better understand the network, based on the similarity, let the nodes select multiple similar nodes to form similar node pairs. Through the Monte Carlo simulation results, a pairing algorithm based on the maximum node similarity and degree is proposed to discover the overlapping community structure of the network. Using multi-level most similarity to continue to optimize the community structure, find out the deep overlapping structure and sub-community structure of the network community. The proposed algorithm discovers the overlapping structure of the network based on the reason why the real network forms a community, and further optimizes the community structure, discovering the deep overlapping community structure of the network and its sub-community structure.

According to the complex correlation characteristics of the actual interdependent network and the important evaluation indicators in the network, five different coupling methods are proposed, and nine interdependent network models are established. Considering the information transmission, redistribution and cascading failures in the network, a cascading failure model based on betweenness artificial flow models is established. We based on the game theory, attack-defense game problems of critical infrastructure are analyzed from the perspective of complex network, and the robustness of various interdependent networks is analyzed. We discovered the preferences of game participants in the interdependent networks, providing decision support for the protection of infrastructure networks.

In order to better represent the rare entities with a small number of neighbors, this paper proposes a knowledge graph embedding model based on the nearest neighbors (NNKGE), which uses the K-Nearest Neighbor algorithm to obtain the nearest neighbors of the target entity as extended information. Based on this, the relational nearest neighbors-based knowledge graph embedding model (RNNKGE) is proposed. To generate an enhanced entity representation, the nearest neighbors of the target entity in relation are obtained by the improved K-Nearest Neighbor algorithm and encoded by the graph memory network. Through the analysis of the experimental results on the public datasets, the above two models outperform the benchmark model (CoNE) in the case of using only the nearest neighbor nodes, alleviating the data sparsity problem and improving the knowledge representation performance.

To scientifically evaluate the resilience of China′s industrial chain and promote high-quality economic development, this paper employs complex network theory and utilizes data from China′s multi-regional input-output table to construct a simulated attack model of the industrial chain network, thereby measuring the resilience of the industrial chain. The results show that the industrial chain layout of 31 provinces in China exhibits obvious local correlation attributes, and presents a collaborative development characteristic led by key regions and industries; The overall resilience of China′s industrial chain is strong, and during the inspection period, the overall resilience index of China′s industrial chain shows an upward trend; There is a significant gap in the resilience of the industrial chain between regions and industries in China.

With the rapid development of the cold chain logistics industry, enterprises are paying more attention to the cost, quality of logistics services and carbon emissions generated by transportation. In order to solve this problem, a multi- depot half-open cold chain logistics routing optimization model is constructed, and various dynamic changes are integrated into the model. An improved NSGA-II algorithm is designed and the standard test function is used to prove that the algorithm has good convergence and diversity. The results show that, compared with the scheme without considering carbon emission, the distribution cost of the scheme with considering carbon emission increases by 19.00%, the transportation distance decreases by 5.16%, and the carbon emission cost decreases by 13.03%. Compared with the initial distribution scheme, the distribution cost of the real-time optimization scheme decreased by 29.78%, the carbon emission cost decreased by 23.19%, and the customer satisfaction increased by 14.49%. Compared with the single distribution center mode, the distribution cost of the multi-depot mode is reduced by 27.30%, the carbon emission cost is reduced by 48.89%, and the customer satisfaction is increased by 13.11%, which brings certain management enlightenment for cold chain logistics enterprises.

This paper tries address the problem of regulatory capture in drug safety governance resulting from information asymmetry and hidden behavior of subjects. To achieve this, this paper employs evolutionary game theory to construct a three-party evolutionary game model that includes the State Drug Administration, the local Drug Administration, and the drug manufacturer. Then the Evolutionary Stability Strategies (ESS) are obtained, and a simulation analysis is conducted to analyze the impact of each parameter on the regulatory capture of the drug administrations. In addition, this paper focuses on the effects of the penalty intensity of the State Drug Administration on the local Drug Administration and drug manufacturers. Based on the findings, recommendations are provided for the government.

Based on evolutionary game theory, this paper establishes an evolutionary game model of collaborative ecological innovation of core enterprises and non-core enterprises in the supply chain under government regulation. And then it analyzes the stability of strategy selection and combines numerical simulation to simulate the impact of different parameter changes on system evolution. The results show that: the higher the income of ecological innovation and the lower the cost, which is conducive to collaborative ecological innovation of enterprises; the free-rider income will have a negative impact on the collaborative ecological innovation of enterprises; there is an optimal co-benefit distribution coefficient that makes the two parties have the greatest probability of collaborative ecological innovation; the government′s subsidy and punishment mechanism has a significant role in promoting the collaborative ecological innovation of enterprises.

This paper proposes an improved ant colony algorithm to solve the problem of slow and poor convergence. First, a correction strategy is introduced, which includes two local correction methods to reduce invalid paths. Second, an adaptive pheromone updating mechanism is developed to distinguish and volatilize the initial pheromone from the pheromone released. For the pheromone released in each iteration, a change law of time-varying volatilization factor is designed to volatilize independently and obtain pheromone volatilization mechanism with adaptive volatilization intensity. Finally, the proposed algorithm is applied to mobile robot path planning. Compared with the existing improved ant colony algorithms, the results show that the improved algorithm is excellent in terms of effective time, average distance and shortest distance.

In order to identify the important weighted motifs in the directed weighted networks, the directed weighted networks are transformed into label networks and the simple motifs are expanded to label motifs by defining the edge weights as strong and weak labels. For the label motifs of the three nodes, the time-consuming procedure of subgraph traversal is replaced by the estimated probability of the corresponding number of the motifs appear in the random networks, and the important label motifs in the directed weighted networks are identified by introducing a dynamic indicator associated with the label motif type. It is applied to the passing networks of Guangdong team and Liaoning team in the 2019—2020 finals of China Basketball Association (CBA). The important passing modes of the teams in the games and the important players in the corresponding modes are obtained. The important label motifs play a significant role in mining the important construction patterns and key nodes of the directed weighted networks.