We investigate opinion dynamics as a stochastic process in social networks. We introduce the stubborn agent in order to determine the impact of network structure on the emergence of consensus. Depending on the fraction of undirected long-range connections, we observe fascinatingly rich dynamical behavior and transitions from disordered to ordered states. In general, we find that the stubborn agent promotes the emergence of consensus due to the so-called “group effect” that facilitates coalescence between separated network components. Agents are also behaviorally constrained Shannon information entropy in networks. However, since agents want to evolve their opinion with Brownian motion, which may in turn impede full consensus, sufficiently frequent long-range links are in such situations crucial for the network to converge into an absorbing phase. Our experimental findings indicate that, for a large range of control parameters, our model yields stable and fluctuating polarized states.

To improve the informationization and intelligence of the energy Internet industry and enhance the capability of knowledge services, it is necessary to organize the energy Internet body of knowledge from existing knowledge resources of the State Grid, which have the characteristics of large scale, multiple sources, and heterogeneity. At the same time, the business fields of State Grid cover a wide range. There are many sub-fields under each business field, and the relationship between fields is diverse and complex. The key to establishing the energy Internet body of knowledge is how to fuse the heterogeneous knowledge resources from multiple sources, extract the knowledge contents from them, and organize the different relationships. This paper considers transforming the original knowledge resources of State Grid into a unified and well-organized knowledge system described in OWL language to meet the requirements of heterogeneous resource integration, multi-source resource organization, and knowledge service provision. For the State Grid knowledge resources mainly in XML format, this paper proposes a Knowledge Automatic Fusion and Organization idea and method based on XSD Directed Graph. According to the method, the XML corresponding XSD documents are transformed into a directed graph in the first stage during which the graph neural network detects hidden knowledge inside the structure to add semantic information to the graph. In the second stage, for other structured knowledge resources (e.g., databases, spreadsheets), the knowledge contents and the relationships are analyzed manually to establish the mappings from structured resources to graph structures, using which the original knowledge resources are transformed into graph structures, and merged with the directed graphs obtained in the first stage to achieve the fusion of heterogeneous knowledge resources. And expert knowledge is introduced for heterogeneous knowledge fusion to further extend the directed graph. And in the third stage, the expanded directed graph is converted to the body of knowledge in the form of OWL. This paper takes the knowledge resources in the field of human resources of the State Grid as an example, to establish the ontology of the human resources training field in a unified manner, initially demonstrating the effectiveness of the proposed method.

The primary reason for detecting emerging topics is to reduce researchers’ time in finding current related topic while maintaining awareness of current trends in a particular field. Nowadays, the amount of information is growing rapidly, but tracking the development of a research field by manually reading the literature is challenging. This study takes Library and Information Science (LIS) as a case study to present a new method for detecting emerging topics. This novel method could be applied to analyse various types of documents and detect emerging topics automatically. This method utilizes a Latent Dirichlet Allocation (LDA) model to generate topics and calculate probabilities. It discovers emerging topics by detecting probability burst in consecutive time spans. Association rule mining and lexical similarity computation are adopted to represent the topics. This work tests the method by comparing the results of emerging topics from the LIS data in the baseline paper. The validation demonstrates that the proposed approach is feasible.