Abstract
Birbiri ile bağlantıları çizgilerle görselleştirilen aktörlerin bir araya gelmesi ile oluşturulan bağlantı haritaları normalde fark edilmeyen bir takım ilişkileri gösterme gücüne sahip araçlardır. İlişkileri haritalandırmak bile anlam çıkarmaya yardım edebilir ancak bu ilişkilerden daha fazla anlam çıkarmanın bir yolu da “ağ analizi” adı verilen ve çizge(graph) teorisine dayalı matematiksel yöntemlerdir. Ağ analizi ile aktörler (insan, şirket, kurum vs.) arasındaki ilişkilerden ağdaki en güçlü aktörler, birbirine aşırı bağlı gruplar (klikler) veya ağdaki izole aktörler ortaya çıkarılabilir. Ağ analizi için UCINET, Gephi gibi araçlar mevcut olmakla birlikte bu araçların tamamı masaüstü uygulaması olarak kurulum gerektirmektedir. Bu araçlardan UCINET ücretli iken, Gephi ise Java Geliştirme Ortamının eski sürümlerini kullanmaktadır. Bu nedenle kurulumları pratik değildir. Çok güçlü olmalarına rağmen bu araçlar sadece kurulum değil kullanım itibariyle de dikkat ve uzmanlık gerektirirler. Bu durum bazı basit analizleri yapmak isteyen alan dışı kullanıcıların işini zorlaştırmaktadır. Bu amaçla, bu makale kapsamında Web3A adlı, web tabanlı bir ağ analiz aracı geliştirilmiştir. Web3A yardımı ile ağa ait tüm merkezilik ölçütleri Web3A web uygulaması üzerinden hesaplanabilmekte ve ağ görselleştirilebilmektedir. Bu araç, her alandan araştırmacıların ağ analizini hızlı ve basit şekilde gerçekleştirmelerini sağlamak için geliştirilmiştir. Bu çalışma, geliştirilen bu aracın çalışma şeklini örnekler üzerinden ortaya koymayı amaçlamaktadır.
References
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Abstract
Network diagramming is a way that have the power to show some relationships that are not normally noticed with standard data visualization technqiues. Even these diagrams can help make sense of them, but one way to extract more meaning from those relationships is "network analysis". With network analysis, the strongest actors in the network, overly connected groups (cliques) or isolated actors in the network can be revealed from the relationships between the actors (human, company, institution, etc.). Although there are tools such as UCINET and Gephi for network analysis, all of these tools require installation as a desktop application. While UCINET is paid, Gephi uses old versions of Java Development Environment. Therefore, their installation is impractical. Although these current applications ar very powerful, these tools require attention and expertise not only in installation but also in use. This makes it difficult for non-expert end-users who want to do some simple analysis. For this purpose, a web-based network analysis tool named Web3A has been developed within the scope of this article. With the help of Web3A, all centrality criteria of the network can be calculated over the Web3A web application and the network can be visualized. This tool has been developed to enable researchers from all fields to perform network analysis quickly and simply. This study aims to demonstrate the working style of this developed tool through examples.
References
- Atan, S., & Emekci, H. (2018). İktisat ve İşletme Uygulamaları İçin R ile Veri Analizi, İstatistik, Modelleme ve Uygulama (1. bs). Seçkin Yayıncılık.
- Baskici, C., Atan, S., & Ercil, Y. (2018). Authors at the boundary: Interaction of local and general scientific literature. Malaysian Journal of Library & Information Science, 23(3), 15–33.
- Bukowski, W. M., Laursen, B., & Rubin, K. H. (2019). Handbook of Peer Interactions, Relationships, and Groups, Second Edition. Guilford Publications.
- Conyon, M. J., & Muldoon, M. R. (2007). Ownership and Control: A Small-World Analysis. 33.
- Ma’ayan, A. (2008). Network integration and graph analysis in mammalian molecular systems biology. IET systems biology, 2(5), 206-221. https://doi.org/10.1049/iet-syb:20070075
- McCulloh, M. I., Garcia, L. G., MacGibbon, L. J., Dye, H., Moores, M. K., & Graham, L. J. (2007). IkeNet: Social Network Analysis of E-mail Traffic in the Eisenhower Leadership Development Program. 51.
- Milgram, S. (1967). The small world problem. Phychology Today, 1, 61–67.
- Moreno, J. L. (1934). Who shall survive? Beacon House.
- Navigli, R., & Ponzetto, S. P. (2010). BabelNet: Building a Very Large Multilingual Semantic Network. Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, 216–225. https://www.aclweb.org/anthology/P10-1023
- Stangor, C. (2004). Social Groups in Action and Interaction. Psychology Press.
- Thomas, S., & Bonchev, D. (2010). A survey of current software for network analysis in molecular biology. Human Genomics, 4(5), 353. https://doi.org/10.1186/1479-7364-4-5-353
- Williamson, S. G. (2010). Lists, Decisions and Graphs. S. Gill Williamson.
- Wittek, M., Kroneberg, C., & Lämmermann, K. (2020). Who is fighting with whom? How ethnic origin shapes friendship, dislike, and physical violence relations in German secondary schools. Social Networks, 60, 34-47. https://doi.org/10.1016/j.socnet.2019.04.004
- Xu, J., & Chen, H. (2005). Criminal network analysis and visualization. Communications of the ACM, 48(6), 100-107. https://doi.org/10.1145/1064830.1064834
Details
| Primary Language | Turkish |
|---|---|
| Subjects | Finance |
| Journal Section | Articles |
| Authors | |
| Publication Date | May 31, 2022 |
| Submission Date | September 20, 2021 |
| Published in Issue | Year 2022 Volume: 24 Issue: 1 |
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