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IEEE TRANSACTIONS ON visualization AND COMPUTER graphICS 2010年第6期16卷 1090-1099页

作者： Riche, Nathalie Henry Dwyer, Tim Microsoft Corp Redmond WA 98052 USA

In many common data analysis scenarios the data elements are logically grouped into sets. Venn and Euler style diagrams are a common visual representation of such set membership where the data elements are represented by labels or glyphs and sets are indicated by boundaries surrounding their members. Generating such diagrams automatically such that set regions do not intersect unless the corresponding sets have a non-empty intersection is a difficult problem. Further, it may be impossible in some cases if regions are required to be continuous and convex. Several approaches exist to draw such set regions using more complex shapes, however, the resulting diagrams can be difficult to interpret. In this paper we present two novel approaches for simplifying a complex collection of intersecting sets into a strict hierarchy that can be more easily automatically arranged and drawn (Figure 1). In the first approach, we use compact rectangular shapes for drawing each set, attempting to improve the readability of the set intersections. In the second approach, we avoid drawing intersecting set regions by duplicating elements belonging to multiple sets. We compared both of our techniques to the traditional non-convex region technique using five readability tasks. Our results show that the compact rectangular shapes technique was often preferred by experimental subjects even though the use of duplications dramatically improves the accuracy and performance time for most of our tasks. In addition to general set representation our techniques are also applicable to visualization of networks with intersecting clusters of nodes.

关键词： Information visualization Euler diagrams Set visualization graph visualization

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Summarizing and Understanding Large graphs

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STATISTICAL ANALYSIS AND DATA MINING 2015年第3期8卷 183-202页

作者： Koutra, Danai Kang, U. Vreeken, Jilles Faloutsos, Christos Carnegie Mellon Univ Sch Comp Sci Pittsburgh PA 15213 USA Korea Adv Inst Sci & Technol Dept Comp Sci Taejon 305701 South Korea Max Planck Inst Informat Databases & Informat Syst D-66123 Saarbrucken Germany Univ Saarland D-66123 Saarbrucken Germany

How can we succinctly describe a million-node graph with a few simple sentences? Given a large graph, how can we find its most "important" structures, so that we can summarize it and easily visualize it? How can we measure the "importance" of a set of discovered subgraphs in a large graph? Starting with the observation that real graphs often consist of stars, bipartite cores, cliques, and chains, our main idea is to find the most succinct description of a graph in these "vocabulary" terms. To this end, we first mine candidate subgraphs using one or more graph partitioning algorithms. Next, we identify the optimal summarization using the minimum description length (MDL) principle, picking only those subgraphs from the candidates that together yield the best lossless compression of the graph-or, equivalently, that most succinctly describe its adjacency matrix. Our contributions are threefold: (i) formulation: we provide a principled encoding scheme to identify the vocabulary type of a given subgraph for six structure types prevalent in real-world graphs, (ii) algorithm: we develop VoG, an efficient method to approximate the MDL-optimal summary of a given graph in terms of local graph structures, and (iii) applicability: we report an extensive empirical evaluation on multimillion-edge real graphs, including Flickr and the Notre Dame web graph. (C) 2015 Wiley Periodicals, Inc. Statistical Analysis and Data Mining: The ASA Data Science Journal 8: 183-202, 2015

关键词： graph summarization minimum description length graph visualization

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SmartGD: A GAN-Based graph Drawing Framework for Diverse Aesthetic Goals

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IEEE TRANSACTIONS ON visualization AND COMPUTER graphICS 2024年第8期30卷 5666-5678页

作者： Wang, Xiaoqi Yen, Kevin Hu, Yifan Shen, Han-Wei Ohio State Univ Columbus OH 43210 USA Yahoo Res New York NY 10003 USA

While a multitude of studies have been conducted on graph drawing, many existing methods only focus on optimizing a single aesthetic aspect of graph layouts, which can lead to sub-optimal results. There are a few existing methods that have attempted to develop a flexible solution for optimizing different aesthetic aspects measured by different aesthetic criteria. Furthermore, thanks to the significant advance in deep learning techniques, several deep learning-based layout methods were proposed recently. These methods have demonstrated the advantages of deep learning approaches for graph drawing. However, none of these existing methods can be directly applied to optimizing non-differentiable criteria without special accommodation. In this work, we propose a novel Generative Adversarial Network (GAN) based deep learning framework for graph drawing, called SmartGD, which can optimize different quantitative aesthetic goals, regardless of their differentiability. To demonstrate the effectiveness and efficiency of SmartGD, we conducted experiments on minimizing stress, minimizing edge crossing, maximizing crossing angle, maximizing shape-based metrics, and a combination of multiple aesthetics. Compared with several popular graph drawing algorithms, the experimental results show that SmartGD achieves good performance both quantitatively and qualitatively.

关键词： Layout graph drawing Deep learning Generative adversarial networks Stress Generators Training data Deep learning for visualization generative adversarial networks graph visualization

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Heritage Iconographic Content Structuring: from Automatic Linking to Visual Validation

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ACM JOURNAL ON COMPUTING AND CULTURAL HERITAGE 2024年第3期17卷 1-32页

作者： Blettery, Emile Gouet-Brunet, Valerie Univ Gustave Eiffel ENSG LASTIG IGN Champs Sur Marne France Ville Paris DAC DHAAP Paris France

This article presents a global framework dedicated to the structuring of iconographic heritage collections. To alleviate the poor interlinking both between collections and contents, a first step of automatic linking exploiting content-based image retrieval approaches is evaluated and adapted to the visual variability of such heritage contents. To ensure understanding and analysis of the contents in a structured fashion, a 3D immersive web platform is also introduced alongside visual-based analysis tools. Finally, by exploiting both automatic linking and manual interventions in the visualization platform, an iterative, semi-automatic structuring pipeline is proposed to solve difficult cases missed by automatic structuring, and then improve structuring optimally. Here, we demonstrate the potential of the proposal on the geographic iconographic heritage of Paris, with a dataset of 10k images belonging to several institutions, thus poorly connected nor organized globally.

关键词： Image retrieval re-ranking data linking graph visualization geographical iconographic heritage

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Interactive, graph-Based Visual Analysis of High-Dimensional, Multi-Parameter Fluorescence Microscopy Data in Toponomics

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IEEE TRANSACTIONS ON visualization AND COMPUTER graphICS 2011年第12期17卷 1882-1891页

作者： Oeltze, Steffen Freiler, Wolfgang Hillert, Reyk Doleisch, Helmut Preim, Bernhard Schubert, Walter Univ Magdeburg D-39106 Magdeburg Germany SimVis GmbH Vienna Austria

In Toponomics, the function protein pattern in cells or tissue (the toponome) is imaged and analyzed for applications in toxicology, new drug development and patient-drug-interaction. The most advanced imaging technique is robot-driven multi-parameter fluorescence microscopy. This technique is capable of co-mapping hundreds of proteins and their distribution and assembly in protein clusters across a cell or tissue sample by running cycles of fluorescence tagging with monoclonal antibodies or other affinity reagents, imaging, and bleaching in situ. The imaging results in complex multi-parameter data composed of one slice or a 3D volume per affinity reagent. Biologists are particularly interested in the localization of co-occurring proteins, the frequency of co-occurrence and the distribution of co-occurring proteins across the cell. We present an interactive visual analysis approach for the evaluation of multi-parameter fluorescence microscopy data in toponomics. Multiple, linked views facilitate the definition of features by brushing multiple dimensions. The feature specification result is linked to all views establishing a focus+context visualization in 3D. In a new attribute view, we integrate techniques from graph visualization. Each node in the graph represents an affinity reagent while each edge represents two co-occurring affinity reagent bindings. The graph visualization is enhanced by glyphs which encode specific properties of the binding. The graph view is equipped with brushing facilities. By brushing in the spatial and attribute domain, the biologist achieves a better understanding of the function protein patterns of a cell. Furthermore, an interactive table view is integrated which summarizes unique fluorescence patterns. We discuss our approach with respect to a cell probe containing lymphocytes and a prostate tissue section.

关键词： Visual Analytics Fluorescence Microscopy Toponomics Protein Interaction graph visualization

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Divided Edge Bundling for Directional Network Data

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IEEE TRANSACTIONS ON visualization AND COMPUTER graphICS 2011年第12期17卷 2354-2363页

作者： Selassie, David Heller, Brandon Heer, Jeffrey Stanford Univ Dept Comp Sci Stanford CA 94305 USA

The node-link diagram is an intuitive and venerable way to depict a graph. To reduce clutter and improve the readability of node-link views, Holten & van Wijk's force-directed edge bundling employs a physical simulation to spatially group graph edges. While both useful and aesthetic, this technique has shortcomings: it bundles spatially proximal edges regardless of direction, weight, or graph connectivity. As a result, high-level directional edge patterns are obscured. We present divided edge bundling to tackle these shortcomings. By modifying the forces in the physical simulation, directional lanes appear as an emergent property of edge direction. By considering graph topology, we only bundle edges related by graph structure. Finally, we aggregate edge weights in bundles to enable more accurate visualization of total bundle weights. We compare visualizations created using our technique to standard force-directed edge bundling, matrix diagrams, and clustered graphs;we find that divided edge bundling leads to visualizations that are easier to interpret and reveal both familiar and previously obscured patterns.

关键词： graph visualization aggregation node-link diagrams edge bundling physical simulation

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Overview with details for exploring geo-located graphs on maps

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INFORMATION visualization 2016年第3期15卷 214-237页

作者： Brodkorb, Felix Kuijper, Arjan Andrienko, Gennady Andrienko, Natalia von Landesberger, Tatiana Tech Univ Darmstadt Fraunhoferstr 5 D-64283 Darmstadt Germany Fraunhofer IGD Darmstadt Germany Fraunhofer IAIS Bonn Germany City Univ London London EC1V 0HB England

Geo-located graph drawings often suffer from map visualization problems, such as overplotting of nodes as well as edges and location of parts of the graph being outside of the screen. One cause of these problems is often an irregular distribution of nodes on the map. Zooming and panning do not solve the problems, as they either only show the overview of the whole graph or only the details of a part of the graph. We present an interactive graph drawing technique that overcomes these problems without affecting the overall geographical structure of the graph. First, we introduce a method that uses insets to visualize details of small or remote areas. Second, to prevent the subgraphs within insets from overplotting and edge crossing, we introduce a local area re-arrangement. Moreover, insets are automatically drawn/ hidden and repositioned in accordance with the user's navigation. We test our technique on real-world geo-located graph data and show the effectiveness of our approach for showing overview and details at the same time. Additionally, we report on expert feedback concerning our approach.

关键词： graph visualization networks geographic networks spatial data navigation focus plus context

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A Deep Generative Model for Reordering Adjacency Matrices

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IEEE TRANSACTIONS ON visualization AND COMPUTER graphICS 2023年第7期29卷 3195-3208页

作者： Kwon, Oh-Hyun Kao, Chiun-How Chen, Chun-houh Ma, Kwan-Liu Univ Calif Davis Dept Comp Sci Davis CA 95616 USA Tamkang Univ Dept Stat New Taipei City 251301 Taiwan Acad Sinica Inst Stat Sci Taipei 11529 Taiwan

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.

关键词： Sorting Data visualization Neural networks Computational modeling Training Computer architecture Stochastic processes graph visualization matrix visualization machine learning deep generative model visualization interface

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A comparison of benchmark task and insight evaluation methods for information visualization

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INFORMATION visualization 2011年第3期10卷 162-181页

作者： North, Chris Saraiya, Purvi Duca, Karen Virginia Polytech Inst & State Univ Dept Comp Sci Blacksburg VA 24061 USA Virginia Polytech Inst & State Univ Virginia Bioinformat Inst Blacksburg VA 24061 USA

This study compares two different empirical research methods for evaluating information visualizations: the traditional benchmark-task method and the insight method. The methods are compared using criteria such as the conclusions about the visualization designs provided by each method, the time participants spent during the study, the time and effort required to analyse the resulting empirical data, and the effect of individual differences between participants on the results. The study compares three graph visualization alternatives that associate bioinformatics microarray time series data to pathway graph vertices in order to investigate the effect of different visual grouping structures in visualization designs that integrate multiple data types. It is confirmed that visual grouping should match task structure, but interactive grouping proves to be a well-rounded alternative. Overall, the results validate the insight method's ability to confirm results of the task method, but also show advantages of the insight method to illuminate additional types of tasks. Efficiency and insight frequently correlate, but important distinctions are found.

关键词： empirical evaluation graph visualization time series data analysis benchmark tasks insight

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New knowledge in strategic management through visually mining semantic networks

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INFORMATION SYSTEMS FRONTIERS 2017年第1期19卷 165-185页

作者： Ertek, Gurdal Tokdemir, Gul Sevinc, Mete Tunc, Murat Mustafa Dubai Silicon Oasis Rochester Inst Technol Dubai Dubai U Arab Emirates Cankaya Univ Dept Comp Engn Fac Engn Eskisehir Yolu 29 Km TR-06810 Ankara Turkey Eindhoven Univ Technol Sch Ind Engn Paviljoen E12POB 513 NL-5600 MB Eindhoven Netherlands Univ Texas Dallas Jindal Sch Management 800 West Campbell Rd Dallas TX 75080 USA

Today's highly competitive business world requires that managers be able to make fast and accurate strategic decisions, as well as learn to adapt to new strategic challenges. This necessity calls for a deep experience and a dynamic understanding of strategic management. The trait of dynamic understanding is mainly the skill of generating additional knowledge and innovative solutions under the new environmental conditions. Building on the concepts of information processing, this paper aims to support managers in constructing new strategic management knowledge, through representing and mining existing knowledge through graph visualization. To this end, a three-stage framework is proposed and described. The framework can enable managers to develop a deeper understanding of the strategic management domain, and expand on existing knowledge through visual analysis. The model further supports a case study that involves unstructured knowledge of profit patterns and the related strategies to succeed using these patterns. The applicability of the framework is shown in the case study, where the unstructured knowledge in a strategic management book is first represented as a semantic network, and then visually mined for revealing new knowledge.

关键词： Knowledge representation Knowledge generation Strategic management Information visualization Semantic networks graph visualization

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