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研究生: Enrico Armando
Enrico Armando
論文名稱: A Mental Map Preserving Visualization Animation via Genetic Algorithm
A Mental Map Preserving Visualization Animation via Genetic Algorithm
指導教授: 楊傳凱
Chuan-Kai Yang
口試委員: 劉顯仲
John S. Liu
林伯慎
Bor-Shen Lin
學位類別: 碩士
Master
系所名稱: 管理學院 - 資訊管理系
Department of Information Management
論文出版年: 2021
畢業學年度: 109
語文別: 英文
論文頁數: 53
中文關鍵詞: Graph VisualizationDynamic Graph DrawingGenetic AlgorithmMental Map PreservationNetwork StructureDrawing Algorithm
外文關鍵詞: Graph Visualization, Dynamic Graph Drawing, Genetic Algorithm, Mental Map Preservation, Network Structure, Drawing Algorithm
相關次數: 點閱:188下載:12
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How video games change through time is quite an interesting topic to be analyzed further. This research focuses on producing an animation of aesthetically-pleasing two dimensional (2D) undirected graphs based on a PC video games dataset to analyze it further and developing a web-based application for users to control and create the animation of graphs. To make an animation of graphs easier to understand, it is preferable that the changes between the displays of the previous period and the next period are as least as possible, thus allowing a user to grasp the changes of the graph’s structure faster. To tackle this problem, a Genetic Algorithm based undirected graph drawing that minimizes both aesthetic criteria and mental map cost is proposed in this research. The dataset used for this research is collected from GameFAQs.com on April, 24th 2019. After a preprocessing, it consists of 36,696 games data with the english titles from 1985 to 2019. Our experiment results proved that smoother animation can be achieved and information is indeed better preserved throughout the animation.

Abstract Acknowledgment Table of Contents List of Figures List of Tables Chapter 1. Introduction 1.1 Background 1.2 Contribution 1.3 Research Outline Chapter 2. Related Works 2.1 Undirected Graph Drawing 2.2 Undirected Graph Drawing as Optimization Problem 2.2.1 Genetic Algorithm for Graph Drawing 2.3 Dynamic Graph and Mental Map Preservation Chapter 3. Proposed System 3.1 System Overview 3.2 System Architecture 3.3 Dataset & Similarity Calculation 3.4 Transition Animation 3.5 Genetic Algorithm 3.5.1 Fitness Function 3.5.2 Genetic Operator 3.6 Mental Map Preservation Chapter 4. Experimental Results 4.1 Experiments Parameter 4.2 Experimental Results 4.3 Mental Map Evaluation Chapter 5. Conclusion and Discussion 5.1 Conclusion References

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