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RANK-BASED INDICES FOR TESTING INDEPENDENCE BETWEEN TWO HIGH-DIMENSIONAL VECTORS

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ANNALS OF STATISTICS 2024年第1期52卷 184-206页

作者： Zhou, Yeqing Xu, Kai Zhu, Liping Li, Runze Tongji Univ Sch Math Sci Shanghai Peoples R China Tongji Univ Key Lab Intelligent Comp & Applicat Shanghai Peoples R China Anhui Normal Univ Sch Math & Stat Hefei Peoples R China Renmin Univ China Inst Stat & Big Data Ctr Appl Stat Beijing Peoples R China Zhejiang Gongshang Univ Zhijiang Inst Big Data & Stat Sch Stat & Math Hangzhou Peoples R China Penn State Univ Dept Stat University Pk PA USA

To test independence between two high-dimensional random vectors, we propose three tests based on the rank-based indices derived from Hoeffding's D, Blum-Kiefer-Rosenblatt's R and Bergsma-Dassios-Yanagimoto's tau(& lowast;). Under the null hypothesis of independence, we show that the distributions of the proposed test statistics converge to normal ones if the dimensions diverge arbitrarily with the sample size. We further derive an explicit rate of convergence. Thanks to the monotone transformation-invariant property, these distribution-free tests can be readily used to generally distributed random vectors including heavily-tailed ones. We further study the local power of the proposed tests and compare their relative efficiencies with two classic distance covariance/correlation based tests in high-dimensional settings. We establish explicit relationships between D, R, tau(& lowast;) and Pearson's correlation for bivariate normal random variables. The relationships serve as a basis for power comparison. Our theoretical results show that under a Gaussian equicorrelation alternative: (i) the proposed tests are superior to the two classic distance covariance/correlation based tests if the components of random vectors have very different scales;(ii) the asymptotic efficiency of the proposed tests based on D, tau(& lowast;) and R are sorted in a descending order.

关键词： Bergsma-Dassios-Yanagimoto's t Blum-Kiefer-Rosenblatt's R degenerate U- statistics Hoeffding's D

Erato: Cooperative data Story Editing via Fact Interpolation

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arXiv 2022年

作者： Sun, Mengdi Cai, Ligan Cui, Weiwei Wu, Yanqiu Shi, Yang Cao, Nan Intelligent Big Data Visualization Lab Tongji University China Microsoft Research Asia China

As an effective form of narrative visualization, visual data stories are widely used in data-driven storytelling to communicate complex insights and support data understanding. Although important, they are difficult to create, as a variety of interdisciplinary skills, such as data analysis and design, are required. In this work, we introduce Erato, a human-machine cooperative data story editing system, which allows users to generate insightful and fluent data stories together with the computer. Specifically, Erato only requires a number of keyframes provided by the user to briefly describe the topic and structure of a data story. Meanwhile, our system leverages a novel interpolation algorithm to help users insert intermediate frames between the keyframes to smooth the transition. We evaluated the effectiveness and usefulness of the Erato system via a series of evaluations including a Turing test, a controlled user study, a performance validation, and interviews with three expert users. The evaluation results showed that the proposed interpolation technique was able to generate coherent story content and help users create data stories more efficiently. Copyright © 2022, The Authors. All rights reserved.

关键词： Interpolation

Affective visualization Design: Leveraging the Emotional Impact of data

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arXiv 2023年

作者： Lan, Xingyu Wu, Yanqiu Cao, Nan The Research Group of Computational and AI Communication Institute for Global Communications and Integrated Media China Intelligent Big Data Visualization Lab Tongji University China

In recent years, more and more researchers have reflected on the undervaluation of emotion in data visualization and highlighted the importance of considering human emotion in visualization design. Meanwhile, an increasing number of studies have been conducted to explore emotion-related factors. However, so far, this research area is still in its early stages and faces a set of challenges, such as the unclear definition of key concepts, the insufficient justification of why emotion is important in visualization design, and the lack of characterization of the design space of affective visualization design. To address these challenges, first, we conducted a literature review and identified three research lines that examined both emotion and data visualization. We clarified the differences between these research lines and kept 109 papers that studied or discussed how data visualization communicates and influences emotion. Then, we coded the 109 papers in terms of how they justified the legitimacy of considering emotion in visualization design (i.e., why emotion is important) and identified five argumentative perspectives. Based on these papers, we also identified 61 projects that practiced affective visualization design. We coded these design projects in three dimensions, including design fields (where), design tasks (what), and design methods (how), to explore the design space of affective visualization design. © 2023, CC BY.

关键词： data visualization

iNet:visual analysis of irregular transition in multivariate dynamic networks

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Frontiers of Computer Science 2022年第2期16卷 124-139页

作者： Dongming HAN Jiacheng PAN Rusheng PAN Dawei ZHOU Nan CAO Jingrui HE Mingliang XU Wei CHEN State Key Lab of CAD&CG Zhejiang UniversityHangzhou 310058China Department of Computer Science and Engineering Arizona State UniversityArizona 85287America Tong Ji Intelligent Big Data Visualisation Lab(iDVx Lab) TongJi UniversityShanghai 200082China Department of Computer Science and Technology Zhengzhou UniversityZhengzhou 450001China

Multivariate dynamic networks indicate networks whose topology structure and vertex attributes are evolving along *** are common in multimedia *** detection is one of the essential tasks in analyzing these networks though it is not well *** this paper,we combine a rare category detection method and visualization techniques to help users to identify and analyze anomalies in multivariate dynamic *** conclude features of rare categories and two types of anomalies of rare *** we present a novel rare category detection method,called DIRAD,to detect rare category candidates with *** develop a prototype system called iNet,which integrates two major visualization components,including a glyph-based rare category identifier,which helps users to identify rare categories among detected substructures,a major view,which assists users to analyze and interpret the anomalies of rare categories in network topology and vertex ***,including an algorithm performance evaluation,a case study,and a user study,are conducted to test the effectiveness of proposed methods.

关键词： multivariate dynamic networks rare categories anomaly detection visual analysis

VizLinter: A linter and fixer framework for data visualization

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arXiv 2021年

作者： Chen, Qing Sun, Fuling Xu, Xinyue Chen, Zui Wang, Jiazhe Cao, Nan Intelligent Big Data Visualization Lab Tongji University

— Despite the rising popularity of automated visualization tools, existing systems tend to provide direct results which do not always fit the input data or meet visualization requirements. Therefore, additional specification adjustments are still required in real-world use cases. However, manual adjustments are difficult since most users do not necessarily possess adequate skills or visualization knowledge. Even experienced users might create imperfect visualizations that involve chart construction errors. We present a framework, VizLinter, to help users detect flaws and rectify already-built but defective visualizations. The framework consists of two components, (1) a visualization linter, which applies well-recognized principles to inspect the legitimacy of rendered visualizations, and (2) a visualization fixer, which automatically corrects the detected violations according to the linter. We implement the framework into an online editor prototype based on Vega-Lite specifications. To further evaluate the system, we conduct an in-lab user study. The results prove its effectiveness and efficiency in identifying and fixing errors for data visualizations. © 2021, CC BY.

关键词： visualization

OpenKG Chain:A Blockchain Infrastructure for Open Knowledge Graphs

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data Intelligence 2021年第2期3卷 205-227页

作者： Huajun Chen Ning Hu Guilin Qi Haofen Wang Zhen Bi Jie Li Fan Yang College of Computer Science&AZFT Knowledge Engine Lab Zhejiang UniversityHangzhou 310058China Ontology Foundation Venture Drive#11-31Vision ExchangeSingapore 608526Singapore School of Computer Science and Engineering Southeast UniversityNanjing 211189China Intelligent Big Data Visualization Lab Tongji UniversityShanghai 200092China

The early concept of knowledge graph originates from the idea of the semantic Web,which aims at using structured graphs to model the knowledge of the world and record the relationships that exist between *** publishing knowledge bases as open data on the Web has gained significant *** China,Chinese Information Processing Society of China(CIPS)launched the OpenKG in 2015 to foster the development of Chinese Open Knowledge *** existing open knowledge-based programs,OpenKG chain is envisioned as a blockchain-based open knowledge *** article introduces the first attempt at the implementation of sharing knowledge graphs on OpenKG chain,a blockchain-based trust *** have completed the test of the underlying blockchain platform,and the on-chain test of OpenKG’s data set and tool set sharing as well as fine-grained knowledge crowdsourcing at the triple *** have also proposed novel definitions:K-Point and OpenKG Token,which can be considered to be a measurement of knowledge value and user value.1,033 knowledge contributors have been involved in two months of testing on the blockchain,and the cumulative number of on-chain recordings triggered by real knowledge consumers has reached 550,000 with an average daily peak value of more than 10,*** the first time,we have tested and realized on-chain sharing of knowledge at entity/triple granularity *** present,all operations on the data sets and tool sets at ***,as well as the triplets at OpenBase,are recorded on the chain,and corresponding value will also be generated and assigned in a trusted *** this effort,OpenKG chain looks forward to providing a more credible and traceable knowledge-sharing platform for the knowledge graph community.

关键词： Open knowledge graph Blockchain Decentralized distributed network Decentralized knowledge graph

ChartKG: A Knowledge-Graph-Based Representation for Chart Images

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arXiv 2024年

作者： Zhou, Zhiguang Wang, Haoxuan Zhao, Zhengqing Zheng, Fengling Wang, Yongheng Chen, Wei Wang, Yong School of Media and Design Hangzhou Dianzi University CO Hangzhou310018 China School of Media and Design Hangzhou Dianzi University China Big Data Visualization and Human Computer Collaborative Intelligent Laboratory Hangzhou Dianzi University China Research Center of Big Data Intelligence Zhejiang Lab Hangzhou China State Key Lab of CAD&CG Zhejiang University China College of Computing and Data Science Nanyang Technological University Singapore

Chart images, such as bar charts, pie charts, and line charts, are explosively produced due to the wide usage of data visualizations. Accordingly, knowledge mining from chart images is becoming increasingly important, which can benefit downstream tasks like chart retrieval and knowledge graph completion. However, existing methods for chart knowledge mining mainly focus on converting chart images into raw data and often ignore their visual encodings and semantic meanings, which can result in information loss for many downstream tasks. In this paper, we propose ChartKG, a novel knowledge graph (KG) based representation for chart images, which can model the visual elements in a chart image and semantic relations among them including visual encodings and visual insights in a unified ***, we develop a general framework to convert chart images to the proposed KG-based representation. It integrates a series of image processing techniques to identify visual elements and relations, e.g., CNNs to classify charts, yolov5 and optical character recognition to parse charts, and rule-based methods to construct graphs. We present four cases to illustrate how our knowledge-graph-based representation can model the detailed visual elements and semantic relations in charts, and further demonstrate how our approach can benefit downstream applications such as semantic-aware chart retrieval and chart question answering. We also conduct quantitative evaluations to assess the two fundamental building blocks of our chart-to-KG framework, i.e., object recognition and optical character recognition. The results provide support for the usefulness and effectiveness of ChartKG. © 2024, CC BY.

关键词： Knowledge graph

Survey on Visual Analysis of Event Sequence data

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arXiv 2020年

作者： Guo, Yi Guo, Shunan Jin, Zhuochen Kaul, Smiti Gotz, David Cao, Nan Intelligent Big Data Visualization Lab Tongji University Shanghai China Visual Analysis and Communication Lab University of North Carolina Chapel HillNC United States

Event sequence data record series of discrete events in the time order of occurrence. They are commonly observed in a variety of applications ranging from electronic health records to network logs, with the characteristics of large-scale, high-dimensional and heterogeneous. This high complexity of event sequence data makes it difficult for analysts to manually explore and find patterns, resulting in ever-increasing needs for computational and perceptual aids from visual analytics techniques to extract and communicate insights from event sequence datasets. In this paper, we review the state-of-the-art visual analytics approaches, characterize them with our proposed design space, and categorize them based on analytical tasks and applications. From our review of relevant literature, we have also identified several remaining research challenges and future research opportunities. Copyright © 2020, The Authors. All rights reserved.

关键词： visualization

Improving visualization interpretation using counterfactuals

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arXiv 2021年

作者： Kaul, Smiti Borland, David Cao, Nan Gotz, David Dept. of Computer Science University of North Carolina at Chapel Hill United States RENCI University of North Carolina at Chapel Hill United States Intelligent Big Data Visualization Lab Tongji University China School of Information and Library Science University of North Carolina at Chapel Hill United States

Complex, high-dimensional data is used in a wide range of domains to explore problems and make decisions. Analysis of high-dimensional data, however, is vulnerable to the hidden influence of confounding variables, especially as users apply ad hoc filtering operations to visualize only specific subsets of an entire dataset. Thus, visual data-driven analysis can mislead users and encourage mistaken assumptions about causality or the strength of relationships between features. This work introduces a novel visual approach designed to reveal the presence of confounding variables via counterfactual possibilities during visual data analysis. It is implemented in CoFact, an interactive visualization prototype that determines and visualizes counterfactual subsets to better support user exploration of feature relationships. Using publicly available datasets, we conducted a controlled user study to demonstrate the effectiveness of our approach;the results indicate that users exposed to counterfactual visualizations formed more careful judgments about feature-to-outcome relationships. Copyright © 2021, The Authors. All rights reserved.

关键词： visualization