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.

The optimization of platform regulation strategy is the key to ensure the compliance and legality of suppliers and anchors in live broadcast e-commerce. Aiming at the interest relationship, behavior strategy and game relationship of suppliers, live broadcasting platform and anchor, this paper constructs the behavior strategy evolution game model of the three, reveals the behavior strategy evolution law of live broadcasting e-commerce under the platform regulation, and uses Netlogo to simulate the strategy evolution process. The research shows that the appropriate punishment and restraint of the platform is helpful to standardize the behavior of suppliers and anchors; Higher platform subsidies can encourage suppliers and anchors to standardize their behavior, but at the expense of their own interests, the system is difficult to stabilize; The illegal income factor is an important factor affecting the supplier's supply strategy and anchor behavior choice. Accordingly, the basic strategies and specific measures to standardize the behavior of suppliers and anchors are put forward.

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.

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.

This paper studied a bi-objective vehicle routing problem with time windows, variable speed, multiple path choice, and capacity constraints (BOVRPTWVDPC) in a complex road network aiming at minimizing the total cost of distribution and maximizing the overall customer satisfaction. In modelling of customer’s satisfaction, the factors of distribution time window and customer priority were taken into account, and in the description of vehicle speed, the traffic period and road conditions were considered. A bi-objective mixed integer programming model was established, and an improved ant colony algorithm was designed to solve the problem. The simulation results show that the proposed model and the improved algorithm are effective and have certain reference value for vehicle distribution path planning under complex road conditions.

Synergy is ubiquitous in contagion processes on complex networks. Most existing studies have been focused on the continuous models, yet the discrete models received less attention. Motivated by this, we employ the generating function method to study a two-state (active or inactive) threshold model on complex networks with different synergistic effects. Compared to the case without synergy, the positive synergy enhances prevalence and weakens systematic robustness. The negative synergy, however, plays an opposite role. These effects are strengthened when the network is heterogeneous.

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 better formulate policies for the development of artificial intelligence, this paper analyzes the technology convergence mechanism in the field of artificial intelligence by constructing the technology convergence network model. Based on the patent data from 2010—2019 of artificial intelligence field, combined with technology and organization dimension, this paper tries to analyzes from three aspects of technical characteristics, organization technical characteristics and organization relationship characteristics. The results show that: In the field of artificial intelligence, organization cooperation is sparse, fusion technology is relatively scattered, organization and technology have obvious core-edge structure characteristics. On the level of technical characteristics, similar technologies are easier to be converged, and technologies that have already been converged will promote new convergence; On the level of organizational technical characteristics, the common technologies owned by the orgnization will negatively affect the occurrence of convergence with other technologies; On the level of organizational relationship characteristics, the effect of cooperation between organizations on technological convergence is closely related to the development stage of the field, and the "circle of buddies" inhibits technology convergence.