Soylu, MucahitDas, Resul2026-04-042026-04-042025979-833158990-5https://doi.org/10.1109/IDAP68205.2025.11222287https://hdl.handle.net/11616/1080739th International Artificial Intelligence and Data Processing Symposium, IDAP 2025 -- 6 September 2025 through 7 September 2025 -- Malatya -- 215321While knowledge graphs are invaluable to fields such as artificial intelligence and data analysis, their size and complexity make them difficult to understand. This study examines how we can make large and complex knowledge graphs more understandable, that is, how we can effectively 'visualize' them. First, we cover basic visualization methods such as node-link diagrams and force-oriented layouts. We compare which method works best when, as well as their advantages and disadvantages. We then focus on interactive techniques that enable users to explore the data independently, beyond simply viewing a static image. We show how features such as filtering, clustering, and zooming simplify and enrich the analysis process with examples. Finally, we discuss today's challenging problems, such as reducing visual clutter and displaying constantly changing (dynamic) data. We conclude by offering a perspective on what opportunities new technologies, such as augmented and virtual reality, can offer in this area. This paper aims to provide both a theoretical foundation and a practical roadmap for anyone interested in knowledge graph visualization. © 2025 IEEE.eninfo:eu-repo/semantics/closedAccessBig DataData VisualizationInteractive AnalysisKnowledge GraphNode-Link DiagramsRelational DataConference Object10.1109/IDAP68205.2025.112222872-s2.0-105025028056N/A