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| 1 | Exploring the limits of complexity: A survey of empirical studies on graph visualisation显示文摘For decades,researchers in information visualisation and graph drawing have focused on developing techniques for the layout and display of very large and complex networks.Experiments involving human participants have also explored the readability of different styles of layout and representations for such networks.In both bodies of literature,networks are frequently referred to as being‘large’or‘complex’,yet these terms are relative.From a human-centred,experiment point-of-view,what constitutes‘large’(for example)depends on several factors,such as data complexity,visual complexity,and the technology used.In this paper,we survey the literature on human-centred experiments to understand how,in practice,different features and characteristics of node–link diagrams affect visual complexity. | Vahan Yoghourdjian Daniel Archambault Stephan Diehl Tim Dwyer Karsten Klein Helen C.Purchase Hsiang-Yun Wu | 2018 | Visual Informatics2018，2，4： | 2 |
| 2 | Cultural diversity in management,firm performance,and the moderating role of entrepreneurial orientation dimensions显示文摘 | Barnett Tim Dwyer Sean Chadwick Ken | 2004 | Academy of Management Journal2004，47，2： | 1 |
| 3 | The Data Visualisation and Immersive Analytics Research Lab at Monash University显示文摘This article reviews two decades of research in topics in Information Visualisation emerging from the Data Visualisation and Immersive Analytics Lab at Monash University Australia(Monash IA Lab).The lab has been influential with contributions in algorithms,interaction techniques and experimental results in Network Visualisation,Interactive Optimisation and Geographic and Cartographic visualisation.It has also been a leader in the emerging topic of Immersive Analytics,which explores natural interactions and immersive display technologies in support of data analytics.We reflect on advances in these areas but also sketch our vision for future research and developments in data visualisation more broadly. | Tim Dwyer Maxime Cordeil Tobias Czauderna Pari Delir Haghighi Barrett Ens Sarah Goodwin Bernhard Jenny Kim Marriott Michael Wybrow | 2020 | Visual Informatics2020，4，4： | 0 |
| 4 | VETA:Visual eye-tracking analytics for the exploration of gaze patterns and behaviours显示文摘Eye tracking is growing in popularity for multiple application areas,yet analysing and exploring the large volume of complex data remains difficult for most users.We present a comprehensive eye tracking visual analytics system to enable the exploration and presentation of eye-tracking data across time and space in an efficient manner.The application allows the user to gain an overview of general patterns and perform deep visual analysis of local gaze exploration.The ability to link directly to the video of the underlying scene allows the visualisation insights to be verified on the fly.The system was motivated by the need to analyse eye-tracking data collected from an‘in the wild’study with energy network operators and has been further evaluated via interviews with 14 eye-tracking experts in multiple domains.Results suggest that,thanks to state-of-the-art visualisation techniques and by providing context with videos,our system could enable an improved analysis of eye-tracking data through interactive exploration,facilitating comparison between different participants or conditions,thus enhancing the presentation of complex data analysis to non-experts.This research paper provides four contributions:(1)analysis of a motivational use case demonstrating the need for rich visual-analytics workflow tools for eye-tracking data;(2)a highly dynamic system to visually explore and present complex eye-tracking data;(3)insights from our applied use case evaluation and interviews with experienced users demonstrating the potential for the system and visual analytics for the wider eye-tracking community. | Sarah Goodwin Arnaud Prouzeau Ryan Whitelock-Jones Christophe Hurter Lee Lawrence Umair Afzal Tim Dwyer | 2022 | Visual Informatics2022，6，2： | 0 |
| 5 | Guidance in the human-machine analytics process显示文摘In this paper,we list the goals for and the pros and cons of guidance,and we discuss the role that it can play not only in key low-level visualization tasks but also the more sophisticated model-generation tasks of visual analytics.Recent advances in artificial intelligence,particularly in machine learning,have led to high hopes regarding the possibilities of using automatic techniques to perform some of the tasks that are currently done manually using visualization by data analysts.However,visual analytics remains a complex activity,combining many different subtasks.Some of these tasks are relatively low-level,and it is clear how automation could play a role—for example,classification and clustering of data.Other tasks are much more abstract and require significant human creativity,for example,linking insights gleaned from a variety of disparate and heterogeneous data artifacts to build support for decision making.In this paper,we outline the potential applications of guidance,as well as the inputs to guidance.We discuss challenges in implementing guidance,including the inputs to guidance systems and how to provide guidance to users.We propose potential methods for evaluating the quality of guidance at different phases in the analytic process and introduce the potential negative effects of guidance as a source of bias in analytic decision making. | Christopher Collins Natalia Andrienko Tobias Schreck Jing Yang Jaegul Choo Ulrich Engelke Amit Jena Tim Dwyer | 2018 | Visual Informatics2018，2，3： | 0 |
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