DBNetVizor: Visual Analysis of Dynamic Basketball Player Networks

IF 7.5 3区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Baofeng Chang;Guodao Sun;Sujia Zhu;Qi Jiang;Wang Xia;Jingwei Tang;Ronghua Liang
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引用次数: 0

Abstract

Visual analysis has been increasingly integrated into the exploration of temporal networks, as visualization methods have the capability to present time-varying attributes and relationships of entities in an easy-to-read manner. Visualization techniques have been employed in a variety of dynamic network datasets, including social media networks, academic citation networks, and financial transaction networks. However, effectively visualizing dynamic basketball player network data, which consists of numerical networks, intensive timestamps, and subtle changes, remains a challenge for analysts. To address this issue, we propose a snapshot extraction algorithm that involves human-in-the-loop methodology to help users divide a series of networks into hierarchical snapshots for subsequent network analysis tasks, such as node exploration and network pattern analysis. Furthermore, we design and implement a prototype system, called DBNetVizor, for dynamic basketball player network data visualization. DBNetVizor integrates a graphical user interface to help users extract snapshots visually and interactively, as well as multiple linked visualization charts to display macro- and micro-level information of dynamic basketball player network data. To demonstrate the usability and efficiency of our proposed methods, we present two case studies based on dynamic basketball player network data in a competition. Additionally, we conduct an evaluation and receive positive feedback.
动态篮球运动员网络的可视化分析
可视化分析已经越来越多地集成到时间网络的探索中,因为可视化方法能够以易于阅读的方式呈现实体的时变属性和关系。可视化技术已应用于各种动态网络数据集,包括社交媒体网络、学术引文网络和金融交易网络。然而,对于分析人员来说,有效地可视化动态篮球运动员网络数据仍然是一个挑战,这些数据由数字网络、密集的时间戳和细微的变化组成。为了解决这个问题,我们提出了一种快照提取算法,该算法涉及人在环方法,以帮助用户将一系列网络划分为分层快照,用于后续的网络分析任务,如节点探索和网络模式分析。此外,我们设计并实现了一个名为DBNetVizor的原型系统,用于动态篮球运动员网络数据可视化。DBNetVizor集成了图形用户界面,帮助用户可视化地、交互式地提取快照,以及多个链接的可视化图表,显示动态篮球运动员网络数据的宏观和微观层面信息。为了证明我们提出的方法的可用性和效率,我们给出了两个基于比赛中动态篮球运动员网络数据的案例研究。此外,我们进行评估并收到积极的反馈。
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来源期刊
CiteScore
11.80
自引率
2.80%
发文量
114
期刊介绍: The IEEE Transactions on Big Data publishes peer-reviewed articles focusing on big data. These articles present innovative research ideas and application results across disciplines, including novel theories, algorithms, and applications. Research areas cover a wide range, such as big data analytics, visualization, curation, management, semantics, infrastructure, standards, performance analysis, intelligence extraction, scientific discovery, security, privacy, and legal issues specific to big data. The journal also prioritizes applications of big data in fields generating massive datasets.
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