Graph Visualization Methods Research Articles

Research Status and Evolutionary Trends of Digitally Empowered Community Emergency Management: Knowledge Graph Analysis Based on CiteSpace

(1) Background: With the acceleration of globalization, various risks are intertwined and coupled, and community emergency management is facing greater pressure than before. The continuous development of digital technology has made it possible to empower community emergency management with digital technology. Therefore, it is urgent to have a clear understanding of the research status and future development direction of digital empowering community emergency management. (2) Methods: This study is based on the Web of Science database and utilizes the CiteSpace knowledge graph visualization method to analyze the research progress of community digital emergency management. (3) Results: The results indicate that from 2000 to 2024, the amount of research on community digital emergency management has been on the rise, involving multiple disciplines, but there has not been a close and mature cooperation network between different countries, institutions, and authors. The research topic has shifted from early single-emergency management to integration with emerging science and technology, with a greater emphasis on the role of communities. Big data, machine learning, technological innovation, digital healthcare, and community resilience are important research directions for the future. (4) Conclusions: In the future, we should strengthen cooperation in this field, overcome issues such as the digital divide and digital security, and promote further development of community digital emergency management.

A new approach to recognizing the use of attitude markers by authors of academic journal articles

This study investigates the use of Attitude Markers(AMs) by native academic authors of English (NAAEs) and Turkish-speaking academic authors of English (TAAEs) in 100 academic articles on Teacher Education. The primary objectives are to examine the forms and functions of AMs used by both groups to indicate their stance in articles and to compare the frequency and variety of AMs used by each group. The study employs a corpus-based approach and adopts a graph visualization method to present the findings. The data were cleaned using a software-supported approach to improve the efficiency of corpus compilation. The data were analyzed using the Antconc text analysis tool (Anthony, 2011) and Log-likelihood statistics. The reliability of the analysis was tested by calculating the inter-rater reliability. To do this, the content coded by one of the researchers and an independent rater was compared using Cohen’s Kappa coefficient. The results ranged from 0.81 to 0.92, indicating a high level of reliability. Later, the findings were visualized using a radial knowledge graph. The statistical analysis showed significant differences in the use of certain AMs between the two groups, including AMs related to “assessment” (-13,20 LL; p<.01) and “significance” (-82,64 LL; p<.01). The findings indicate that both NAAEs and TAAEs commonly use AMs to convey their stance, with ‘significance’ and ‘assessment’ being the most frequent functional categories, and ‘adjective’ being the most commonly used form of AMs in both corpora. The findings provide valuable insights into the use of AMs in academic writing and can inform the development of English for academic purposes (EAP) course materials to enhance the academic writing skills of novice writers. The results of the study suggest that future research could use graph visualization to carry our corpus studies and could explore the effectiveness of using Artificial Intelligence (AI) technologies to minimize human bias in qualitative analyses.

Hashtags as structural elements of digital socio-political agenda: folksonomy analysis

The situation of the recent months, associated with constitutional transformational changes and struggle with coronavirus spread, showed that civic communications outperform the government as regards the speed, directions and the scale of discursive fields generation. The digital socio-political agenda forms new socio-cognitive patterns of civic behavior, which leads to the development of constructive or destructive socio-political practices. The government needs innovational approaches and complex methods to conduct timely predictive analysis of socio-political sentiment. It is necessary to develop computational linguistics that allows for Data Science and relational sociology methods, as well as linguo-discursive analysis to be used in order to assess the current state of the digital socio-political agenda and identify the transformational vector of social action in the offline space. The article reports the results of the study of ‘Коронавирус’ digital socio-political agenda that was conducted through the use of graph visualization method, folksonomy and linguo-discursive analysis. The empirical base for the study comprises a bulk of network data retrieved from Twitter via API. The findings of the study prove that ‘Коронавирус’ digital socio-political agenda has significantly transformed and expanded within a 5 months’ period, with COVID-19 topic receiving twice as much attention from online users.

DEVELOPING SEMANTIC NETWORK MANAGEMENT SYSTEM

The paper considers semantic networks. It is a way of representing knowledge as a set of nodes-concepts connected by edges-relations. The history and current state-of-the-art of semantic networks is analyzed. The experience of designing and developing semantic network management system is described in four sections covering the following topics: data structures design, semantic analysis algorithm implementation, graph visualization methods selection and Web-based user interface development. The developed system is fully-operational tool for building and studying semantic networks. The system will be used for the further research.

Mapping family connectedness across space and time

Understanding the structure and evolution of family networks embedded in space and time is crucial for various fields such as disaster evacuation planning and provision of care to the elderly. Computation and visualization can potentially play a key role in analyzing and understanding such networks. Graph visualization methods are effective in discovering network patterns; however, they have inadequate capability in discovering spatial and temporal patterns of connections in a network especially when the network exists and changes across space and time. We introduce a measure of family connectedness that summarizes the dynamic relationships in a family network by taking into account the distance (how far individuals live apart), time (the duration of individuals’ coexistence within a neighborhood), and the relationship (kinship or kin proximity) between each pair of individuals. By mapping the family connectedness over a series of time intervals, the method facilitates the discovery of hot spots (hubs) where family connectedness is strong and the changing patterns of such spots across space and time. We demonstrate our approach using a data set of nine families from the US North. Our results highlight that family connectedness reflects changing demographic processes such as migration and population growth.