期刊論文
| 學年 | 110 |
|---|---|
| 學期 | 2 |
| 出版（發表）日期 | 2022-02-25 |
| 作品名稱 | A Deep Generative Model for Reordering Adjacency Matrices |
| 作品名稱（其他語言） | |
| 著者 | Oh-Hyun Kwon; Chiun-How Kao; Chun-Houh Chen; Kwan-Liu Ma |
| 單位 | |
| 出版者 | |
| 著錄名稱、卷期、頁數 | IEEE Transactions on Visualization and Computer Graphics |
| 摘要 | Depending on the node ordering, an adjacency matrix can highlight distinct characteristics of a graph. Deriving a "proper" node ordering is thus a critical step in visualizing a graph as an adjacency matrix. Users often try multiple matrix reorderings using different methods until they find one that meets the analysis goal. However, this trial-and-error approach is laborious and disorganized, which is especially challenging for novices. This paper presents a technique that enables users to effortlessly find a matrix reordering they want. Specifically, we design a generative model that learns a latent space of diverse matrix reorderings of the given graph. We also construct an intuitive user interface from the learned latent space by creating a map of various matrix reorderings. We demonstrate our approach through quantitative and qualitative evaluations of the generated reorderings and learned latent spaces. The results show that our model is capable of learning a latent space of diverse matrix reorderings. Most existing research in this area generally focused on developing algorithms that can compute "better" matrix reorderings for particular circumstances. This paper introduces a fundamentally new approach to matrix visualization of a graph, where a machine learning model learns to generate diverse matrix reorderings of a graph. |
| 關鍵字 | Sorting;Data visualization;Neural networks;Computational modeling;Training;Computer architecture;Stochastic processes |
| 語言 | en_US |
| ISSN | 1941-0506 |
| 期刊性質 | 國外 |
| 收錄於 | SCI |
| 產學合作 | |
| 通訊作者 | |
| 審稿制度 | 否 |
| 國別 | USA |
| 公開徵稿 | |
| 出版型式 | ,電子版 |
| 相關連結 |
機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/122636 ) |