Journals / Açıköğretim Uygulamaları ve Araştırmaları Dergisi / 2026 / Cilt: 12 Sayı: 2

Creating a video narrative network graphs using a "narrative experience base" approach.

"Anlatı deneyim tabanı" yaklaşımıyla video anlatı ağı çizgeleri oluşturma

Mehmet Emin Mutlu(ANADOLU ÜNİVERSİTESİ)0000-0002-0949-4057
Ayşe Peri Mutlu(ANADOLU ÜNİVERSİTESİ)0000-0003-4690-0760
Pages
97–114
Publication Date
2026-07-06
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The videograph software developed as a result of the research process, which was conducted using a design-based research approach, was tested on a sample video and the findings were evaluated.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Narrative networks are network graphs that allow for the simultaneous visualization of the structure and content of narratives.
📊
Key Findings & Data:A narrative network is created by identifying elements such as characters, events, and locations in a narrative and visualizing the connections that define their relationships with each other.
💡
Conclusion & Impact:The videograph software developed as a result of the research process, which was conducted using a design-based research approach, was tested on a sample video and the findings were evaluated.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Anlatı Ağları · Anlatı Deneyim Tabanı · Video Anlatı Ağı

Abstract

Narrative networks are network graphs that allow for the simultaneous visualization of the structure and content of narratives. A narrative network is created by identifying elements such as characters, events, and locations in a narrative and visualizing the connections that define their relationships with each other. This process also allows for the visualization of the differentiation of the network along the time axis according to event time and narrative time. When identifying the elements that make up narratives, it is possible to create a "narrative experience base", a special database, by utilizing the concept of experience contexts. In this way, all the experiences the characters have within the narrative are recorded in their entirety and in their full context. By visualizing the narrative experience base in the form of a force-oriented network graph, an interactive digital model of the narrative is obtained. This study aims to investigate and develop the process of creating a video narrative network graph by applying the narrative experience base approach, previously tested for text-format story narratives, to video-format narratives. The videograph software developed as a result of the research process, which was conducted using a design-based research approach, was tested on a sample video and the findings were evaluated.

Özet

Anlatı ağları anlatıların yapısını ve içeriğini aynı anda görselleştirmeye olanak sağlayan ağ çizgeleridir. Bir anlatı ağı anlatıdaki karakterler, olaylar, mekânlar vb. öğelerin belirlenmesi ve birbirleriyle ilişkilerini tanımlayacak bağlantıların görselleştirilmesiyle oluşturulur. Bu süreç olay zamanı ve anlatı zamanına göre zaman ekseninde ağın farklılaşmasını da görüntülemeye olanak sağlar. Anlatıları oluşturan öğeleri belirlerken deneyim bağlamları kavramından yararlanılarak özel bir veri tabanı olan bir "anlatı deneyim tabanı" oluşturmak mümkündür. Böylece anlatıdaki karakterlerin öyküde yaşadıkları tüm deneyimler bütün bağlamlarıyla kayda alınmış olur. Anlatı deneyim tabanının kuvvet yönlendirmeli ağ çizgesi biçiminde görselleştirilmesiyle anlatının etkileşimli dijital bir modeli elde edilir. Bu çalışmada daha önce metin formatındaki öykü anlatıları için denenen anlatı deneyim tabanı yaklaşımının video formatındaki anlatılar için uygulanarak bir video anlatı ağı çizgesi oluşturma sürecinin araştırılması ve geliştirilmesi amaçlanmıştır. Tasarım tabanlı araştırma yaklaşımıyla yürütülen araştırma süreci sonucunda geliştirilen videograf yazılımı örnek bir video üzerinde denenerek elde edilen bulgular değerlendirilmiştir.

🔖

Cite This Article

Mutlu, M. E., Mutlu, A. P. (2026). Creating a video narrative network graphs using a "narrative experience base" approach.. Açıköğretim Uygulamaları ve Araştırmaları Dergisi, Cilt: 12 Sayı: 2, 97–114. https://doi.org/10.51948/auad.1956161

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (22)

  1. Barry, H. (2023), Narrative chart for American Gods S3E01, https://observablehq.com/@hugoberry/narrative-chart-for-american-gods-s3e01 Erişim tarihi: 15 Mayıs 2026
  2. Blender Foundation (2012), Tears of Steel (Full Movie), https://mango.blender.org/ Erişim tarihi: 15 Mayıs 2026
  3. Bostock, M., Ogievetsky, V. & Heer, J. (2011). D3 – data-driven documents. IEEE Transactions on Visualization and Computer Graphics, 17(12):2301–2309. https://doi.org/10.1109/TVCG.2011.185
  4. Bratanic, T. (2020), Exploratory network analysis of Marvel Universe, https://medium.com/data-science/exploratory-network-analysis-of-marvel-universe-c557f4959048 Erişim tarihi: 15 Mayıs 2026
  5. Genette, G. (2011). Anlatının söylemi: Yöntem hakkında bir deneme. Çev. Ferit Burak Aydar, İstanbul: Boğaziçi Üniversitesi. (İlk baskı: 1980).
  6. Heller, S., Gsteiger, V., Bailer, W., Gurrin, C., Jónsson, B. Þ., Lokoč, J.,. & Wu, J. (2022). Interactive video retrieval evaluation at a distance: comparing sixteen interactive video search systems in a remote setting at the 10th Video Browser Showdown. International Journal of Multimedia Information Retrieval, 11(1), 1-18. https://doi.org/10.1007/s13735-021-00225-2
  7. Hoai, M., & Zisserman, A. (2014, November). Thread-safe: Towards recognizing human actions across shot boundaries. In Asian Conference on Computer Vision (pp. 222-237). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-16817-3_15
  8. Hussein, N., Gavves, E., & Smeulders, A. W. (2019). Videograph: Recognizing minutes-long human activities in videos. arXiv preprint arXiv:1905.05143. https://doi.org/10.48550/arXiv.1905.05143
  9. Knuth, D. E. (1993) Les miserables: coappearance network of characters in the novel les miserables, The Stanford GraphBase: A Platform for Combinatorial Computing, Addison-Wesley, Reading, MA
  10. Lee, H., & Chen, J. (2021). Narratives as networks: Predicting memory from the structure of naturalistic events. BioRxiv, 2021.04. 24.441287. https://doi.org/10.1101/2021.04.24.441287
  11. Mandalihan, J. (2023), Exploring the Marvel Universe Through Network Analysis: A Starter’s Guide, https://medium.com/@jmandalihan.msds2023/exploring-the-marvel-universe-through-network-analysis-a-starters-guide-88eb40e85c47 Erişim tarihi: 15 Mayıs 2026
  12. Mayer, R. E. (2020). Multimedia Learning (3rd ed.). Cambridge University Press. https://doi.org/10.1007/978-1-4419-8126-4
  13. Mutlu, M.E, Peri Mutlu, A. (2020). Anlatı Deneyim Tabanları – “Küçük Prens” Örnek Olayı. Gümüşhane Üniversitesi Sosyal Bilimler Enstitüsü Elektronik Dergisi, 11(1), 95-116.
  14. Mutlu, M.E. (2015). Öğrenme Deneyimleri Bağlam Modeli, Eğitim ve Öğretim Araştırmaları Dergisi, 4(3), 173-191.
  15. Özcan, Ö., & Gerçek, C. (2019). Multidimensional analyzing of the microteaching applications in teacher education via videograph. European Journal of Teacher Education, 42(1), 82-97. https://doi.org/10.1080/02619768.2018.1546285
  16. Pan, J. Y., & Faloutsos, C. (2001, January). VideoGraph: A new tool for video mining and classification. In Proceedings of the 1st ACM/IEEE-CS joint conference on Digital libraries (pp. 116-117). https://doi.org/10.1145/379437.379462
  17. Porteous, J., Charles, F., & Cavazza, M. (2013, May). NetworkING: using character relationships for interactive narrative generation. In Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems (pp. 595-602). https://doi.org/10.5555/2484920.2485015
  18. Rossetto, L., Baumgartner, M., Ashena, N., Ruosch, F., Pernisch, R., Heitz, L., & Bernstein, A. (2021, June). VideoGraph–Towards Using Knowledge Graphs for Interactive Video Retrieval. In International Conference on Multimedia Modeling (pp. 417-422). Springer, Cham. https://doi.org/10.1007/978-3-030-67835-7_38
  19. Sweller, J., Ayres, P., & Kalyuga, S. (2011). Cognitive Load Theory. New York: Springer. https://doi.org/10.1007/978-1-4419-8126-4
  20. Venturini, T., Bounegru, L., Jacomy, M., & Gray, J. (2016). How to Tell Stories with Networks: Exploring the Narrative Affordances of Graphs with the Iliad. In Datafied Society: Social Research in the Age of Big Data. (pp. 595-602). Amsterdam University Press. https://doi.org/10.1007/978-3-030-67835-7_38
  21. Wang, F., Hannafin, M. J. (2005). Design-Based Research and Technology-Enhanced Learning Environments, Educational Technology Research and Development, 53(4), 5-23. https://doi.org/10.1007/BF02504682
  22. Heller, S., Gsteiger, V., Bailer, W., Gurrin, C., Jónsson, B. Þ., Lokoč, J.,. & Wu, J. (2022). Interactive video retrieval evaluation at a distance: comparing sixteen interactive video search systems in a remote setting at the 10th Video Browser Showdown. International Journal of Multimedia Information Retrieval, 11(1), 1-18. https://doi.org/10.1007/s13735-021-00225-2