The conditions for the spontaneous emergence of cooperation under the complex human behavior model have become the focus of many disciplines. Exploring the conditions for cooperation has both important scientific significance and theoretical value for understanding the institutional arrangements in human society. The social dilemma games provide a theoretical prototype for studying cooperation issues between multiple individuals. As a dynamic analysis method that can describe individuals′ learning and strategy adjustment processes, evolutionary game theory has been one of the most effective frameworks for studying the evolution of cooperation. This review article systematically summarizes the research progress of using the evolutionary game method to study the issues of group cooperation in social dilemma games. Specifically, the following topics are included: research progress of (1) social dilemma game models, evolutionary game theory and equilibrium analysis methods, (2) social dilemma games and the evolution of cooperation under reward/punishment mechanism and reputation mechanism, (3) social dilemma games and the evolution of cooperation with separation strategy and extortion strategy, and (4) social dilemma games and the evolution of cooperation under the network reciprocity. Finally, prospects for further research issues in this area are presented.

Urban Metro and conventional bus carry a significant portion of residents' daily travel services, and their disruption due to sudden incidents often results in widespread and profound impacts. To ensure safe and efficient operation of public transportation, based on complex network theory, a method is proposed from the perspective of structural resilience for identifying key stations and routes of urban metro and conventional bus networks through importance, that is the resilience-based mean square deviation-TOPSIS comprehensive evaluation method. The reliability of this method is respectively verified through the monotonicity of the importance evaluation results, the robustness analysis of different attack strategies, and the comparative analysis of construction timelines. The case study results show that this method can well differentiate each station in the network; when conducting robustness analysis, it can also reflect the characteristic that key stations with greater importance have a larger impact on the overall network performance; the K-means clustering results of the importance of Shenzhen metro lines are generally consistent with the construction timeline. The reliability of this method in identifying key stations and routes is verified comprehensively.

Complex criminal cases today present systematic characteristics of multi-factor coupling and dynamic evolution, and their investigation process faces the challenge of nonlinear information integration. Criminal suspects use anti-detection methods such as changing clothes and shoes, facial obstruction, and posture camouflage, combined with complex environmental interference, which significantly reduces the practical effectiveness of single technical means such as face recognition and video structuring. In order to resolve this problem, this article focuses on the actual needs of suspect identification and tracking, breaks through the recognition bottleneck of a single modality, systematically integrates multi-information such as video structuring, face recognition, and gait recognition, and proposes a multi-information fusion video investigation system with gait recognition as the core, which realizes the dual characterization of suspect behavior patterns and identity characteristics, and provides a new technical path for improving identity recognition capabilities and the efficiency of solving complex cases.

In this paper, we use generative adversarial network (GAN) to improve semantic segmentation of images. The model is composed of a semantic segmentation network and a discriminant network, where the segmentation network responses for generating semantic segmentation result while the discriminant network responses for detecting the difference between the generated result and the labels on the global structure level and improving the segmentation effect. In order to extract context information, we adopt the spatial pyramid pooling module in the segmentation network, which could perform pooling operation on multiple levels of sub-regions. Meanwhile, in order to solve the problem of a large number of manual annotations needed in the semantic segmentation data set, we use the discriminant network to generate pseudo labels and realize semi-supervision in the training of the segmentation network. The model has been tested using PASCAL VOC2012 dataset, and the results show that supervised and semi-supervised approaches proposed in this paper are superior to the existing methods.

In order to study the impact of stop-loss trading on traders′ behavior and asset prices in the market, this paper constructs a multi-agent market model with stop-loss strategy based on the method of agent-based computational finance. The model simulation results show that when the stop-loss threshold is touched in the market, it is easy to trigger continuous stop-loss trading, resulting in a behavioral cascade between traders. This kind of transaction cascading leads to an increase in the convergence of trader behavior, an imbalance between sell orders and buy orders in the market, an abnormal collapse in market prices and a liquidity black hole.

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.

To explore the behavioral characteristics of massive crowds under the active interaction of millions of streamers and viewers in the field of live streaming, this paper summarized the temporal patterns of live streaming workload and user behavior characteristics of the live streaming platform, taking Douyu and Huya live streaming platforms as examples, a statistical analysis of 123 consecutive days, involving more than 2.4 million anchors, and more than 726 million live streaming data. The live streaming workload has obvious intra-day and intra-week effect. Different live streaming modes have significant differences in live streaming characteristics such as the average number of viewers and followers. The lifetime of streamers and the number of viewers conform to a power law distribution. With the development of the platform, there is a strong linear correlation between the number of streamers and viewers, but its volatility is gradually increasing, reflecting the increasingly strong heterogeneity and non-uniformity of the system. It is of great significance for understanding user behavior patterns in complex systems of live streaming, mining user distribution laws and changing trends, and designing business models such as personalized recommendations.

In this study, behavioral attitudes, subjective norms and perceived behavioral control are used as mediators to explore the role of consumption motivation and cognitive level in shaping consumers' purchase behavior for green agricultural products. A survey of 432 supermarket consumers in Wuhan shows that they have a high willingness to buy safe agricultural products. The consumption motivation and cognitive level of agricultural products' quality and safety level have a positive impact on consumers' purchase behavior through behavior attitudes, subjective norms and perceived behavioral control.Consumer behavior and attitude affects the subjective norms. However, the mediating effects of behavior attitude and subjective norms are independent among consumers' consumption motivation, cognitive level and purchase behavior. Consumer behavior and attitude do not transfer to specific purchase intention and behavior through subjective norms, which provides decision-making reference for government in the supervision of agricultural product quality and safety.

Combined with the topological characteristics and electrical characteristics, this paper proposes an integrated multi-attribute decision method for identifying key nodes in a power grid. Firstly, based on the complex network theory, several evaluation indicators are proposed and calculated considering the topology characteristics and the electrical characteristics of a power grid, an evaluation matrix is obtained. Then the final decision matrix is obtained by weighting the evaluation matrix combined with the analytic hierarchy process and the coefficient of variation method. Finally, the TOPSIS method combined with grey correlation degree is used to calculate the ranking of the important nodes in the power grid.In order to compare the advantages and disadvantages of different identification methods, the network efficiency and synchronization performance are adopted. An actual local power grid is used to further verify the effectiveness a feasibility of the proposed method.

In the majority of cases, networks are beneficial. However, many times it may also be harmful, such as terrorist networks and disease spreading networks. It has become an urgent challenging problem to disintegrate these harmful networks by various methods such as immunization, block, isolation, disturbance, and attack. The core task of network disintegration is to identify the “critical nodes (edges)”. This survey firstly gives the mathematical description of network disintegration. On this basis, this survey then reviews the status of network disintegration study in the fields of operations research, network science, and computer science based on mathematical programming, the centrality metrics, the heuristic algorithms, evolutionary computation, and machine learning, respectively. Lastly, this survey presents the prospects of network disintegration study from the aspects of the target network, disintegration model, and algorithm.