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.

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 explore the usage behavior and cause of emojis on social media, we analyzed the use of emojis in 1 800 958 microblogs under the topic of "Kunshan Case" on Sina Weibo. First, we analysis the frequency of emoji to study the phenomenon of repeated usage of emoji in the group, and then we classify popular emojis and micro-blog texts, we analyze the diversity of emoji usage of individual users. The result shows that: There are a lot of emojis in Weibo and the frequency of emojis is long-tailed and follow Zipf's Law; The evolution of popular emojis can reflect the public opinion of the event; Individual users are used to using 2~3 same emojis or different emojis with similar emotions. The emojis used by users are often related to the topics they express and are influenced by the psychology of herds, and the phenomenon of co-occurrence emojis is usually to strengthen the emotions expressed.

As a form of public opinion, view and attitude, opinion widely exists in people′s life. It is important to clarify the evolution mechanism of opinion, explicit the existing research progress, and promote the rational governance of public opinion. In view of the lack of relevant introduction of binary opinion dynamics and the separation of the relationship between binary and group opinion dynamics, this paper summarized the research status of opinion dynamics at home and abroad. Firstly, it introduces the binary opinion dynamics model from the perspective of research method and interaction characteristic; Secondly, it combed the research results of group opinion dynamics from the perspective of individual characteristic, behavior characteristic, opinion characteristic, external environment and perspective dynamics. Finally, based on the existing research, it clarifies the problems, the mechanism of opinion evolution from the empirical perspective, the strengthening of opinions and the reduction of disputes, and the relationship between the evolution of views and group decision-making, that need to be solved in the future.

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.

The enhancement of the robustness of complex networks has been a hotspot in the field of network science in recent years. It is both of great scientific significance and theoretical value to explore the strategy of enhancing the robustness for network structure design and function improvement. On the basis of extensive collation and systematic analysis of domestic and foreign literature, this paper summarizes comprehensively the key point and main ideasof the current research on the enhancement strategies of complex networks robustness from three aspects: pre-defense, in-process recovery and post-optimization. The advantages, disadvantages and applicability of different strategies are compared and analyzed. Then we look forward to future research direction in this field.

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.

In order to ensure that urban emergency supplies can be delivered to the demand point in a timely and accurate manner, especially for special emergency supplies with a short life cycle′ perishable goods, its timeliness requirements are higher. Based on the emergency scenario of sudden public health events, this paper establishes a multi-objective location model of urban perishable goods distribution center with the goal of minimizing transportation time and transportation cost and maximizing relative coverage area. The Harris Hawk optimization algorithm (HHO) is improved to achieve an effective solution to the multi-objective location problem of perishable goods distribution center. In order to verify the effectiveness of the model, a district in Shanghai is selected as a research example. The results show that the improved HHO algorithm can solve the location model of perishable goods distribution center under actual urban road conditions, and can provide an intuitive multi-objective location optimization scheme.

In order to explore the evolution law of innovation in electronic information industry, the collaborative innovation network is constructed from the perspective of social network. Based on the patent data from 1985—2017, the evolution path and spatial pattern trend of collaborative innovation is analyzed. The results show that electronic information industry has entered a stage of rapid development with enhanced network connectivity and obvious core-edge structure. The cooperative mode has gradually changed from institute-oriented to enterprise-oriented collaborative innovation mode with universities and institutes as knowledge partners. The spatial pattern is characterized by unbalanced development, and the inter-regional cooperation shows a radiation-type network form dominated by the core region.