Pathfinder: Visual Analysis of Paths in Graphs.
Pathfinder: Visual Analysis of Paths in Graphs.
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DOI：
10.1111/cgf.12883
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发表时间：
2016-06
期刊：
影响因子：
--
通讯作者：
Lex A
中科院分区：
文献类型：
--
作者：
Partl C;Gratzl S;Streit M;Wassermann AM;Pfister H;Schmalstieg D;Lex A
图中路径的分析在许多领域都有重要意义。通常，与路径相关的任务在节点链接布局中执行。不幸的是，图形布局通常不能扩展到许多真实的世界网络的大小。此外，许多网络是多变量的，即，包含与节点和边相关联的丰富属性集。这些属性在判断路径时通常是至关重要的，但是直接在图形布局中可视化属性会加剧可伸缩性问题。在本文中，我们提出了可视化分析解决方案，致力于在大型和高度多元图的路径相关的任务。我们表明，通过专注于路径，我们可以解决多变量图形可视化的可扩展性问题，为分析师提供了一个强大的工具来探索大型图形。我们介绍了Pathfinder（图1），这是一种提供可视化方法来查询路径的技术，同时考虑了各种约束。所得到的路径集在排名列表和节点链接图中都是可视化的。对于列表中的路径，我们显示与节点和边关联的丰富属性数据，并且节点链接图提供拓扑上下文。路径可以基于拓扑属性（诸如路径长度或平均节点度）以及从属性数据导出的分数来排名。Pathfinder旨在通过采用增量查询结果等策略扩展到具有数万个节点和边的图形。我们证明了探路者的健身方案中使用的数据从合著者网络和生物途径。
The analysis of paths in graphs is highly relevant in many domains. Typically, path-related tasks are performed in node-link layouts. Unfortunately, graph layouts often do not scale to the size of many real world networks. Also, many networks are multivariate, i.e., contain rich attribute sets associated with the nodes and edges. These attributes are often critical in judging paths, but directly visualizing attributes in a graph layout exacerbates the scalability problem. In this paper, we present visual analysis solutions dedicated to path-related tasks in large and highly multivariate graphs. We show that by focusing on paths, we can address the scalability problem of multivariate graph visualization, equipping analysts with a powerful tool to explore large graphs. We introduce Pathfinder (Figure 1), a technique that provides visual methods to query paths, while considering various constraints. The resulting set of paths is visualized in both a ranked list and as a node-link diagram. For the paths in the list, we display rich attribute data associated with nodes and edges, and the node-link diagram provides topological context. The paths can be ranked based on topological properties, such as path length or average node degree, and scores derived from attribute data. Pathfinder is designed to scale to graphs with tens of thousands of nodes and edges by employing strategies such as incremental query results. We demonstrate Pathfinder's fitness for use in scenarios with data from a coauthor network and biological pathways.
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DOI：
10.1109/tvcg.2014.2346248
发表时间：
2014-12
影响因子：
5.2
作者：
Lex A;Gehlenborg N;Strobelt H;Vuillemot R;Pfister H
通讯作者：
Pfister H
DOI：
10.1109/tvcg.2013.173
发表时间：
2013-12
影响因子：
5.2
作者：
Gratzl S;Lex A;Gehlenborg N;Pfister H;Streit M
通讯作者：
Streit M
影响因子：
2.5
作者：
Greilich, Martin;Burch, Michael;Diehl, Stephan
通讯作者：
Diehl, Stephan
DOI：
10.1093/bioinformatics/btq675
发表时间：
2011-02-01
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Smoot ME;Ono K;Ruscheinski J;Wang PL;Ideker T
通讯作者：
Ideker T
影响因子：
64.8
作者：
Barretina, Jordi;Caponigro, Giordano;Stransky, Nicolas;Venkatesan, Kavitha;Margolin, Adam A.;Kim, Sungjoon;Wilson, Christopher J.;Lehar, Joseph;Kryukov, Gregory V.;Sonkin, Dmitriy;Reddy, Anupama;Liu, Manway;Murray, Lauren;Berger, Michael F.;Monahan, John E.;Morais, Paula;Meltzer, Jodi;Korejwa, Adam;Jane-Valbuena, Judit;Mapa, Felipa A.;Thibault, Joseph;Bric-Furlong, Eva;Raman, Pichai;Shipway, Aaron;Engels, Ingo H.;Cheng, Jill;Yu, Guoying K.;Yu, Jianjun;Aspesi, Peter, Jr.;de Silva, Melanie;Jagtap, Kalpana;Jones, Michael D.;Wang, Li;Hatton, Charles;Palescandolo, Emanuele;Gupta, Supriya;Mahan, Scott;Sougnez, Carrie;Onofrio, Robert C.;Liefeld, Ted;MacConaill, Laura;Winckler, Wendy;Reich, Michael;Li, Nanxin;Mesirov, Jill P.;Gabriel, Stacey B.;Getz, Gad;Ardlie, Kristin;Chan, Vivien;Myer, Vic E.;Weber, Barbara L.;Porter, Jeff;Warmuth, Markus;Finan, Peter;Harris, Jennifer L.;Meyerson, Matthew;Golub, Todd R.;Morrissey, Michael P.;Sellers, William R.;Schlegel, Robert;Garraway, Levi A.
通讯作者：
Garraway, Levi A.