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17th Pacific visualization Conference (PacificVis)

作者： Han, Dongming Zhu, Haiyang Chen, Wei Pan, Jiacheng Chen, Ran Wang, Xumeng Wen, Zhen Weng, Luoxuan Zhu, Minfeng Wu, Yingcai Westermann, Rudiger Zhejiang Univ State Key Lab CAD CG Hangzhou Peoples R China Hithink RoyalFlush Informat Network Co Ltd Hangzhou Peoples R China Zhejiang Univ Lab Art & Archaeol Image Minist Educ Hangzhou Peoples R China Nankai Univ Coll Comp Sci Tianjin Peoples R China Zhejiang Univ Hangzhou Peoples R China Tech Univ Munich Comp Graph & Visualizat Munich Germany Wuchan Zhongda Digital Technol Co Ltd Hangzhou Peoples R China

ISBN: (纸本)9798350393811;9798350393804

Authoring graph visualization requires advanced programming skills, expert domain knowledge, and significant workload. Existing authoring tools either support limited templates of graph visualization, or suffer from a high learning cost. We analyze the design requirements on a tool for graph visualizations, and contribute Nuwa, a user-friendly declarative authoring tool for the interactive specification of graph visualizations in terms of data, entity, change, and encoding. Our implementation empowers users to conveniently create, compare, and modulate comprehensive graph visualizations with a wide range of styles. We showcase various examples to verify the expressiveness of Nuwa. Via an expert interview and the analysis on cognitive dimensions we evaluate the usability of Nuwa.

关键词： graph visualization Authoring tool

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Using Cliques with Higher-order Spectral Embeddings Improves graph visualizations 20

Using Cliques with Higher-order Spectral Embeddings Improves...

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29th World Wide Web Conference (WWW)

作者： Nassar, Huda Kennedy, Caitlin Jain, Shweta Benson, Austin R. Gleich, David F. Stanford Univ Stanford CA 94305 USA Purdue Univ W Lafayette IN 47907 USA Univ Calif Santa Cruz Santa Cruz CA 95064 USA Cornell Univ Ithaca NY 14853 USA

ISBN: (纸本)9781450370233

In the simplest setting, graph visualization is the problem of producing a set of two-dimensional coordinates for each node that meaningfully shows connections and latent structure in a graph. Among other uses, having a meaningful layout is often useful to help interpret the results from network science tasks such as community detection and link prediction. There are several existing graph visualization techniques in the literature that are based on spectral methods, graph embeddings, or optimizing graph distances. Despite the large number of methods, it is still often challenging or extremely time consuming to produce meaningful layouts of graphs with hundreds of thousands of vertices. Existing methods often either fail to produce a visualization in a meaningful time window, or produce a layout colorfully called a "hairball", which does not illustrate any internal structure in the graph. Here, we show that adding higher-order information based on cliques to a classic eigenvector based graph visualization technique enables it to produce meaningful plots of large graphs. We further evaluate these visualizations along a number of graph visualization metrics and we find that it outperforms existing techniques on a metric that uses random walks to measure the local structure. Finally, we show many examples of how our algorithm successfully produces layouts of large networks. Code to reproduce our results is available.

关键词： graph visualization graph layout spectral methods higher-order methods cliques sampling

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Reading Strategies for graph visualizations thatWrap Around in Torus Topology 23

Reading Strategies for Graph Visualizations thatWrap Around ...

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15th Annual ACM Symposium on Eye Tracking Research and Applications (ETRA)

作者： Chen, Kun-Ting Ngo, Quynh Quang Kurzhals, Kuno Marriott, Kim Dwyer, Tim Sedlmair, Michael Weiskopf, Daniel Univ Adelaide Australia Adelaide SA Australia Univ Stuttgart Stuttgart Germany Monash Univ Clayton Vic Australia

ISBN: (纸本)9798400701504

We investigate reading strategies for node-link diagrams that wrap around the boundaries in a flattened torus topology by examining eye tracking data recorded in a previous controlled study. Prior work showed that torus drawing affords greater flexibility in clutter reduction than traditional node-link representations, but impedes link-and-path exploration tasks, while repeating tiles around boundaries aids comprehension. However, it remains unclear what strategies users apply in different wrapping settings. This is important for design implications for future work on more effective wrapped visualizations for network applications, and cyclic data that could benefit from wrapping. We perform visual-exploratory data analysis of gaze data, and conduct statistical tests derived from the patterns identified. Results show distinguishable gaze behaviors, with more visual glances and transitions between areas of interest in the non-replicated layout. Full-context has more successful visual searches than partial-context, but the gaze allocation indicates that the layout could be more space-efficient.

关键词： Eye tracking graph visualization scanpath analysis area of interest

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Exploring Relationships between Annotated Images with the Chaingraph visualization 1

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4th International Conference on Semantic and Digital Media Technologies

作者： Lohmann, Steffen Heim, Philipp Tetzlaff, Lena Ertl, Thomas Ziegler, Juergen Univ Duisburg Essen Interact Syst & Interact Design Lotharstr 65 D-47057 Duisburg Germany Univ Stuttgart Visualisat & Interact Syst D-70569 Stuttgart Germany

ISBN: (数字)9783642105432

ISBN: (纸本)9783642105425

Understanding relationships and commonalities between digital contents based on metadata is a difficult user task that requires sophisticated presentation forms. In this paper, we describe an advanced graph visualization that supports users with these activities. It reduces several problems of common graph visualizations and provides a specific chain arrangement of nodes that facilitates visual tracking of relationships. We present a concrete implementation for the exploration of relationships between images based on shared tags. An evaluation with a comparative user study shows good performance results on several dimensions. We therefore conclude that the Chaingraph approach can be considered a serious alternative to common graph visualizations in situations where relationships and commonalities between contents are of interest. After a discussion of the limitations, we finally point to some application scenarios and future enhancements.

关键词： graph visualization interactive exploration relationship discovery exploratory search user-annotated images visual tracking shared metadata tagging photo sharing annotation

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Multigraph visualization for feature classification of brain network data

Multigraph visualization for feature classification of brain...

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作者： Wang, Jiachen Purdue University

学位级别：M.S.

A Multigraph is a set of graphs with a common set of nodes but different sets of edges. Multigraph visualization has not received much attention so far. In this thesis, I will introduce an interactive application in brain network data analysis that has a strong need for multigraph visualization. For this application, multigraph was used to represent brain connectome networks of multiple human subjects. A volumetric data set was constructed from the matrix representation of the multigraph. A volume visualization tool was then developed to assist the user to interactively and iteratively detect network features that may contribute to certain neurological conditions. I applied this technique to a brain connectome dataset for feature detection in the classification of Alzheimer's Disease (AD) patients. Preliminary results showed significant improvements when interactively selected features are used.

关键词： graph visualization multigraph volume rendering brain imaging feature detection

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Measuring effectiveness of graph visualizations: A cognitive load perspective

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INFORMATION visualization 2009年第3期8卷 139-152页

作者： Huang, Weidong Eades, Peter Hong, Seok-Hee Australian Council Educ Res Natl & Int Surveys Program Camberwell Vic 3124 Australia Univ Sydney Sch Informat Technol Sydney NSW 2006 Australia

graph visualizations are typically evaluated by comparing their differences in effectiveness, measured by task performance such as response time and accuracy. Such performance-based measures have proved to be useful in their own right. There are some situations, however, where the performance measures alone may not be sensitive enough to detect differences. This limitation can be seen from the fact that the graph viewer may achieve the same level of performance by devoting different amounts of cognitive effort. In addition, it is not often that individual performance measures are consistently in favor of a particular visualization. This makes design and evaluation difficult in choosing one visualization over another. In an attempt to overcome the above-mentioned limitations, we measure the effectiveness of graph visualizations from a cognitive load perspective. Human memory as an information processing system and recent results from cognitive load research are reviewed first. The construct of cognitive load in the context of graph visualization is proposed and discussed. A model of user task performance, mental effort and cognitive load is proposed thereafter to further reveal the interacting relations between these three concepts. A cognitive load measure called mental effort is introduced and this measure is further combined with traditional performance measures into a single multi-dimensional measure called visualization efficiency. The proposed model and measurements are tested in a user study for validity. Implications of the cognitive load considerations in graph visualization are discussed. Information visualization (2009) 8, 139 - 152. doi: 10.1057/ivs.2009.10

关键词： graph visualization cognitive load visualization efficiency visualization effectiveness evaluation

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Preserving Minority Structures in graph Sampling

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IEEE TRANSACTIONS ON visualization AND COMPUTER graphICS 2021年第2期27卷 1698-1708页

作者： Zhao, Ying Jiang, Haojin Chen, Qi'an Qin, Yaqi Xie, Huixuan Wu, Yitao Liu, Shixia Zhou, Zhiguang Xia, Jiazhi Zhou, Fangfang Cent South Univ Sch Comp Sci & Engn Changsha Peoples R China Tsinghua Univ Sch Software Beijing Peoples R China Zhejiang Univ Finance & Econ Sch Informat Hangzhou Peoples R China

Sampling is a widely used graph reduction technique to accelerate graph computations and simplify graph visualizations. By comprehensively analyzing the literature on graph sampling, we assume that existing algorithms cannot effectively preserve minority structures that are rare and small in a graph but are very important in graph analysis. In this work, we initially conduct a pilot user study to investigate representative minority structures that are most appealing to human viewers. We then perform an experimental study to evaluate the performance of existing graph sampling algorithms regarding minority structure preservation. Results confirm our assumption and suggest key points for designing a new graph sampling approach named mino-centric graph sampling (MCGS). In this approach, a triangle-based algorithm and a cut-point-based algorithm are proposed to efficiently identify minority structures. A set of importance assessment criteria are designed to guide the preservation of important minority structures. Three optimization objectives are introduced into a greedy strategy to balance the preservation between minority and majority structures and suppress the generation of new minority structures. A series of experiments and case studies are conducted to evaluate the effectiveness of the proposed MCGS.

关键词： Measurement Image edge detection Acceleration visualization Bridges Visual perception Optimization graph sampling graph visualization node-link diagram

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Visualizing Dataflow graphs of Deep Learning Models in TensorFlow

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IEEE TRANSACTIONS ON visualization AND COMPUTER graphICS 2018年第1期24卷 1-12页

作者： Wongsuphasawat, Kanit Smilkov, Daniel Wexler, James Wilson, Jimbo Mane, Dandelion Fritz, Doug Krishnan, Dilip Viegas, Fernanda B. Wattenberg, Martin Univ Washington Paul G Allen Sch Comp & Engn Seattle WA 98195 USA Google Res Mountain View CA USA

We present a design study of the TensorFlow graph Visualizer, part of the TensorFlow machine intelligence platform. This tool helps users understand complex machine learning architectures by visualizing their underlying dataflow graphs. The tool works by applying a series of graph transformations that enable standard layout techniques to produce a legible interactive diagram. To declutter the graph. we decouple non-critical nodes from the layout. To provide an overview, we build a clustered graph using the hierarchical structure annotated in the source code. To support exploration of nested structure on demand. we perform edge bundling to enable stable and responsive cluster expansion. Finally, we detect and highlight repeated structures to emphasize a model's modular composition. To demonstrate the utility of the visualizer, we describe example usage scenarios and report user feedback. Overall, users find the visualizer useful for understanding, debugging, and sharing the structures of their models.

关键词： Neural Network graph visualization Dataflow graph Clustered graph

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graphs Drawn With Some Vertices per Face: Density and Relationships

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IEEE ACCESS 2024年 12卷 68828-68846页

作者： Binucci, Carla Di Battista, Giuseppe Didimo, Walter Dujmovic, Vida Hong, Seok-Hee Kaufmann, Michael Liotta, Giuseppe Morin, Pat Tappini, Alessandra Univ Perugia Dept Engn I-06123 Perugia Italy Univ Roma Tre Dept Civil Comp Sci & Aeronaut Technol Engn I-00154 Rome Italy Univ Ottawa Sch Comp Sci & Elect Engn Ottawa ON K1N 6N5 Canada Univ Sydney Sch Comp Sci Sydney NSW 2050 Australia Univ Tubingen Wilhelm Schickard Inst Informat D-72076 Tubingen Germany Carleton Univ Sch Comp Sci Ottawa ON K1S 5B6 Canada

graph drawing beyond planarity is a research area that has received an increasing attention in the last twenty years, driven by the necessity to mitigate the visual complexity inherent in geometric representations of non-planar graphs. This research area stems from the study of graph layouts with forbidden crossing configurations, a well-established subject in geometric and topological graph theory. In this context, the contribution of this paper is as follows: 1) We introduce a new hierarchy of graph families, called k(+) -real face graphs;for any integer k >= 1 , a graph G is a k(+) -real face graph if it admits a drawing Gamma in the plane such that the boundary of each face (formed by vertices, crossings, and edges) contains at least k vertices of G (" k(+) " stands for k or more);2) We give tight upper bounds on the edge density of k(+) -real face graphs, namely we prove that n-vertex 1+ -real face and 2+ -real face graphs have at most 5n-10 and 4n-8 edges, respectively. Furthermore, in a constrained scenario in which all vertices must lie on the boundary of the external face, 1+ -real face and 2+ -real face graphs have at most 3n-6 and 2.5n-4 edges, respectively;3) We characterize the complete graphs that admit a k(+) -real face drawing or an outer k(+) -real face drawing for any k >= 1 . We also provide a clear picture for the majority of complete bipartite graphs;and 4) We establish relationships between k(+) -real face graphs and other prominent beyond-planar graph families;notably, we show that for any k >= 1 , the class of k(+) -real face graphs is not included in any family of beyond-planar graphs with hereditary property.

关键词： Beyond-planar graph drawing edge density geometric graph theory graph visualization inclusion relationships

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Visualizing and understanding graph convolutional network

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MULTIMEDIA TOOLS AND APPLICATIONS 2021年第6期80卷 8355-8375页

作者： Shi, Xiaoying Lv, Fanshun Seng, Dewen Zhang, Jiaming Chen, Jing Xing, Baixi Hangzhou Dianzi Univ Sch Comp Sci & Technol Key Lab Complex Syst Modeling & Simulat Hangzhou Peoples R China

The graph convolutional network (GCN), which can handle graph-structured data, is enjoying great interest in recent years. However, while GCN achieved remarkable results for different kinds of tasks, the source of its performance and the underlying decision process remain poorly understood. In this paper, we propose a visual analytics system that supports progressive analysis of GCN executing process and the effect of graph convolution operation. Multiple coordinated views are designed to show the influence of hidden layer parameters, the change of loss/accuracy and activation distributions, and the diffusion process of correctly predicted nodes. In particular, since the traditional t-SNE and force-directed layout methods are unable to show the graph-structured data well, we propose to utilize 'graphTSNE', a novel visualization technique for graph-structured data, to present the node layout in a clearer way. The real-world graph dataset is used to demonstrate the usability and effectiveness of our system through case studies. The results manifest that our system can provide sufficient guidance for understanding the working principle of graph convolutional network.

关键词： graph convolutional network Visual analytics graph visualization Interactive exploration

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