Full Text:   <328>

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CLC number: TP311

On-line Access: 2020-04-21

Received: 2019-06-24

Revision Accepted: 2019-11-26

Crosschecked: 2020-01-30

Cited: 0

Clicked: 838

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Jia-cheng Pan

https://orcid.org/0000-0002-8676-9990

Wei Chen

https://orcid.org/0000-0002-8365-4741

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Frontiers of Information Technology & Electronic Engineering  2020 Vol.21 No.4 P.491-506

http://doi.org/10.1631/FITEE.1900310


RCAnalyzer: visual analytics of rare categories in dynamic networks


Author(s):  Jia-cheng Pan, Dong-ming Han, Fang-zhou Guo, Da-wei Zhou, Nan Cao, Jing-rui He, Ming-liang Xu, Wei Chen

Affiliation(s):  State Key Lab of CAD & CG, Zhejiang University, Hangzhou 310058, China; more

Corresponding email(s):   panjiacheng@zju.edu.cn, chenvis@zju.edu.cn

Key Words:  Rare category detection, Dynamic network, Visual analytics


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Jia-cheng Pan, Dong-ming Han, Fang-zhou Guo, Da-wei Zhou, Nan Cao, Jing-rui He, Ming-liang Xu, Wei Chen. RCAnalyzer: visual analytics of rare categories in dynamic networks[J]. Frontiers of Information Technology & Electronic Engineering, 2020, 21(4): 491-506.

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journal="Frontiers of Information Technology & Electronic Engineering",
volume="21",
number="4",
pages="491-506",
year="2020",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1900310"
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Abstract: 
A dynamic network refers to a graph structure whose nodes and/or links dynamically change over time. Existing visualization and analysis techniques focus mainly on summarizing and revealing the primary evolution patterns of the network structure. Little work focuses on detecting anomalous changing patterns in the dynamic network, the rare occurrence of which could damage the development of the entire structure. In this study, we introduce the first visual analysis system RCAnalyzer designed for detecting rare changes of sub-structures in a dynamic network. The proposed system employs a rare category detection algorithm to identify anomalous changing structures and visualize them in the context to help oracles examine the analysis results and label the data. In particular, a novel visualization is introduced, which represents the snapshots of a dynamic network in a series of connected triangular matrices. Hierarchical clustering and optimal tree cut are performed on each matrix to illustrate the detected rare change of nodes and links in the context of their surrounding structures. We evaluate our technique via a case study and a user study. The evaluation results verify the effectiveness of our system.

RCAnalyzer:动态网络中稀有类可视分析系统


潘嘉铖1,2,韩东明1,2,郭方舟1,周大为3,曹楠4, 何京芮3,徐明亮5,6,陈为1,2
1浙江大学计算机辅助设计和图形学国家重点实验室,中国杭州市,310058
2之江实验室,中国杭州市,311100
3亚利桑那州立大学计算机科学与工程学院,美国坦佩市,85281
4同济大学智能大数据可视化实验室,中国上海市,200082
5郑州大学信息工程学院,中国郑州市,450001
6郑州大学河南先进技术研究院,中国郑州市,450001

摘要:动态网络是指其节点和/或链接随时间动态变化的图结构。现有可视化和分析技术主要集中在总结和揭示网络结构的主要演化模式。很少有工作专注于检测动态网络中的异常变化模式,这种情况很少发生,一旦发生,则可能破坏整个结构的发展。本文介绍了第一个可视分析系统RCAnalyzer,用于检测动态网络中子结构的罕见变化。所提系统采用稀有类别检测算法识别异常变化的结构,并在上下文中将其可视化,以帮助专家检查分析结果并标记数据。特别地,引入新的可视化形式,用一系列连接的三角矩阵表达动态网络快照。在每个矩阵上进行层次聚类和最佳树切割,以展示在其周围结构的上下文中检测到的节点和链接的罕见变化。通过案例和用户调研评估该技术。评估结果验证了该系统的有效性。

关键词:稀有类检测;动态网络;可视分析

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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