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    A key review on graph data science: The power of graphs in scientific studies
    (Elsevier, 2023) Das, Resul; Soylu, Mucahit
    This comprehensive review provides an in-depth analysis of graph theory, various graph types, and the role of graph visualization in scientific studies. Graphs serve as powerful tools for modeling and analyzing complex systems in diverse disciplines. The introduction highlights the importance of graphs as a visual representation in scientific research, enabling a better understanding of complex data. Infographics and knowledge graphs have gained significant popularity in recent years due to their effectiveness in conveying information. The review starts by exploring the foundations of graph theory, covering key concepts, algorithms, and applications. It discusses the different types of graphs, including directed, undirected, weighted, and bipartite graphs, and their specific use cases in scientific studies. Special attention is given to special graphs, such as complete graphs, trees, and social networks, which have unique properties and play a significant role in various scientific domains. The review showcases their applications and contributions in fields like biology, social sciences, network analysis, and data mining. Graph visualization emerges as a crucial aspect of understanding and interpreting complex data structures. The review emphasizes the challenges and advancements in graph visualization techniques, enabling researchers to effectively communicate and analyze graph-based information. In conclusion, this comprehensive review serves as a valuable resource for researchers in understanding the principles and applications of graph theory in scientific studies. The exploration of graph types, special graphs, and graph visualization techniques provides insights into the diverse uses and potential of graphs in various scientific disciplines.
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    A new approach to recognizing the use of attitude markers by authors of academic journal articles
    (Pergamon-Elsevier Science Ltd, 2023) Soylu, Mucahit; Soylu, Ayfer; Das, Resul
    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.