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.

To improve the ability to predict the evolution trend of public opinion, a public opinion evolution trend prediction model based on an improved wolf pack algorithm and optimized long-short term memory neural network is proposed. Use Halton Sequence to initialization to improve population diversity. Design step factor to perform Gauss-Sine perturbation transformation to improve wolf group exploration and development capabilities. Combine with the spiral in the whale optimization algorithm to improve the siege mechanism to enhance the local search ability of wolves. The bidirectional memory population is used to increase the cooperative ability of the wolf pack. The improved wolf pack algorithm (IWPA) is applied to the hyperparameter prediction of the LSTM neural network. Using keywords such as “COVID-19” and “Food Safety”, the experiment proves that the IWPA-LSTM neural network public opinion evolution prediction model has good accuracy and generality. The model is suitable for the prediction of various public opinion evolution trends.

The topological characteristics and resilience analysis of public transportation systems are of great significance in urban management to ensure its safe and sustainable operation. This paper constructs a bus-metro interdependent network model based on the passenger transfer relationship and uses deep learning to identify their network topology attributes. A comprehensive importance indicator of the nodes is established by entropy weight-technique for order preference by similarity to ideal solution (EWM-TOPSIS), and the resilience of the network under different recovery strategies are analyzed. In order to verify the applicability and accuracy of the method, this study takes Wuhan's public transportation network as an example, which has practical guiding significance for the post-disaster recovery and operation management of the urban public transportation system.

Invulnerability analysis is one of the core contents of power grid security research. Traditional analysis methods cannot effectively analyze the process of failure generation, and have limitations in the research of invulnerability analysis. This paper studies the invulnerability analysis of power networks based on complex network theory, and conducts an empirical analysis using Chongming District of Shanghai as an example. For random attacks and selective attacks on the power network, the changes in the network efficiency and the maximum number of connected subgraphs after the attack are obtained, and the network efficiency change rate is proposed as a parameter to evaluate the invulnerability. According to the simulation results, a segmented protection scheme based on real-time centrality closeness priority attack strategy is proposed to improve the invulnerability of the power network and ensure the safety of the power grid.

In order to promote the development of autonomous driving technology, this paper focuses on the lane-changing decision-making behavior of autonomous vehicles. First, the lane-changing intention is quantified objectively, and then the lane-changing collision probability and the lane-changing dynamic risky coefficient are introduced. Based on the game theory, the decision-making behavior model of the lane-changing game for autonomous vehicles is established. Besides, speed gains are considered as the objective of game gains. Therefore, autonomous vehicles can change lanes in a coordinated, safe and reasonable manner. Finally, with SUMO software, the traditional LC2013 lane-changing model and the decision-making behavior model of the lane-changing game are used for simulation experiments and comparative analysis. The results show that the decision-making behavior model of the lane-changing game has higher stability, reliability, safety and lane utilization.

To reveal the characteristics and influencing factors of cross-platform information diffusion in social media, this paper took the Legitimate Defense Case in Kunshan as an example and studied the characteristics and related factors of information diffusion from other platforms to Sina Weibo using statistical inference and regression analysis methods. We found that users are more inclined to the latter when balancing the high amount of information in microblogs with the convenience of obtaining information, and the transmissibility, basic reproductive number and diffusion depth of cross-platform information are significantly lower than those of non-cross-platform information. Information from WeChat official accounts, Weibo videos, Weibo articles and Sina news has more advantages over other types of information in terms of depth and scale of diffusion. Compared with ordinary users, users who are authenticated as media and government administration spread information from news platforms on a larger scale. A comprehensive consideration of the types and source platforms of information can help us to better understand the spreading of sudden social events in the Internet space, thus helping to effectively guide or control the evolution of public opinion.

Aiming at the problems of inaccurate keywords extraction and only considering single correlation in subject words extraction, a subject words extraction algorithm combining integration idea with complex network is proposed. Firstly, the keywords of topic data are extracted through the integration algorithm to improve the accuracy of keywords extraction. Secondly, the traditional word co-occurrence formula is improved to calculate the co-occurrence degree of keywords, and a keywords co-occurrence network is established. Based on the network, the optimal connected subgraph is obtained. At the same time, the importance of keywords is measured by taking the centrality of node degree as the weight, and the subject words are mapped. Finally, the micro-blog topic data set is used to verify the example, which proves that the algorithm is effective and better than the traditional word co-occurrence algorithm, and it is applied in the Qingdao community topic data set.

The frequency of bin entry and exit is one of the key factors affecting the efficiency of the “part-to-picker” picking system based on the shuttle storage system. In case of sufficient goods in the bin, the bins of a certain kind of goods required by the same batch of orders only need to be shipped out once. To allocate similar orders to one batch and to reduce the number of bins in and out of the warehouse can improve the picking efficiency of the system. Taking the minimum number of bins out of the warehouse as the objective function, an order batching optimization model is established. According to the characteristics of the model, an improved genetic algorithm is designed. A hybrid crossover strategy is proposed. On the basis of elite retention, partial search is performed on part of the elite chromosomes of each generation with a certain probability to improve the convergence speed and solution accuracy of genetic algorithm. The simulation results show that the total number of outgoing of bins is reduced after optimization, and the picking efficiency of the system is improved and the approach proposed is valid.

In order to explore the impact of network structure and knowledge base on enterprise innovation performance, the cooperative innovation network of industry-university-research institute is constructed from the perspective of social network, and the negative binomial regression model is used for empirical analysis. The results show that the industry-university-research innovation network centrality has a significant positive impact on enterprise innovation performance, network structure hole does not show a significant inverted U-shaped relationship with enterprise innovation performance, knowledge base has a significant positive impact on enterprise innovation performance, and the interaction between knowledge base and network centrality has a significant negative impact on enterprise innovation performance, the interaction between knowledge base and structure hole has a positive effect on enterprise innovation performance, but it is not significant.

After the outbreak of COVID-19, it is of great significance to find an appropriate dynamic model of COVID-19 epidemic in order to master its transmission law, predict its development trend, and provide corresponding prevention and control basis. In this paper, the SEIRV chamber model is adopted, and the dynamics model of infectious disease is established by combining the fractional derivative of Conformable. The fractional derivative differential equation of Conformable is discretized by numerical method and its numerical solution is obtained. In addition, numerical simulation was carried out on the confirmed data of Wuhan city from January 23, 2020 to February 11, 2020. At the same time, consider that the Wuhan municipal government revised the epidemic data on February 12, 2020, adding nearly 14,000 people. The order α value of SEIRV model is modified, and then the revised data is simulated. The simulation results are in good agreement with the published data. The results show that compared with the traditional integer order model, the fractional order model can simulate the modified data. This reflects the advantages of fractional infectious disease dynamics model, and can provide certain reference value for the prediction of COVID-19 model.