The Readability of Path-Preserving Clusterings of Graphs
Graph visualization systems often exploit opaque metanodes to reduce visual clutter and improve the readability of large graphs. This filtering can be done in a path‐preserving way based on attribute values associated with the nodes of the graph. Despite extensive use of these representations, as fa...
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| Vydané v: | Computer graphics forum Ročník 29; číslo 3; s. 1173 - 1182 |
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| Hlavní autori: | , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Oxford, UK
Blackwell Publishing Ltd
01.06.2010
Wiley |
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| ISSN: | 0167-7055, 1467-8659 |
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| Abstract | Graph visualization systems often exploit opaque metanodes to reduce visual clutter and improve the readability of large graphs. This filtering can be done in a path‐preserving way based on attribute values associated with the nodes of the graph. Despite extensive use of these representations, as far as we know, no formal experimentation exists to evaluate if they improve the readability of graphs.
In this paper, we present the results of a user study that formally evaluates how such representations affect the readability of graphs. We also explore the effect of graph size and connectivity in terms of this primary research question. Overall, for our tasks, we did not find a significant difference when this clustering is used. However, if the graph is highly connected, these clusterings can improve performance. Also, if the graph is large enough and can be simplified into a few metanodes, benefits in performance on global tasks are realized. Under these same conditions, however, performance of local attribute tasks may be reduced. |
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| AbstractList | Graph visualization systems often exploit opaque metanodes to reduce visual clutter and improve the readability of large graphs. This filtering can be done in a path‐preserving way based on attribute values associated with the nodes of the graph. Despite extensive use of these representations, as far as we know, no formal experimentation exists to evaluate if they improve the readability of graphs.
In this paper, we present the results of a user study that formally evaluates how such representations affect the readability of graphs. We also explore the effect of graph size and connectivity in terms of this primary research question. Overall, for our tasks, we did not find a significant difference when this clustering is used. However, if the graph is highly connected, these clusterings can improve performance. Also, if the graph is large enough and can be simplified into a few metanodes, benefits in performance on global tasks are realized. Under these same conditions, however, performance of local attribute tasks may be reduced. AbstractGraph visualization systems often exploit opaque metanodes to reduce visual clutter and improve the readability of large graphs. This filtering can be done in a path-preserving way based on attribute values associated with the nodes of the graph. Despite extensive use of these representations, as far as we know, no formal experimentation exists to evaluate if they improve the readability of graphs. Graph visualization systems often exploit opaque metanodes to reduce visual clutter and improve the readability of large graphs. This filtering can be done in a path-preserving way based on attribute values associated with the nodes of the graph. Despite extensive use of these representations, as far as we know, no formal experimentation exists to evaluate if they improve the readability of graphs. In this paper, we present the results of a user study that formally evaluates how such representations affect the readability of graphs. We also explore the effect of graph size and connectivity in terms of this primary research question. Overall, for our tasks, we did not find a significant difference when this clustering is used. However, if the graph is highly connected, these clusterings can improve performance. Also, if the graph is large enough and can be simplified into a few metanodes, benefits in performance on global tasks are realized. Under these same conditions, however, performance of local attribute tasks may be reduced. [PUBLICATION ABSTRACT] Graph visualization systems often exploit opaque metanodes to reduce visual clutter and improve the readability of large graphs. This filtering can be done in a path-preserving way based on attribute values associated with the nodes of the graph. Despite the extensive use these representations, as far as we know, no formal experimentation exists to evaluate if they improve the readability of graphs. In this paper, we present the results of a user study that formally evaluates how such representations affect the readability of graphs. We also explore the effect of graph size and connectivity in terms of this primary research question. Overall, for our tasks, we did not find a significant difference when this clustering is used. However, if the graph is highly connected, these clusterings can improve performance. Also, if the graph is large enough and can be simplified into a few metanodes, benefits in performance on global tasks are realized. Under these same conditions, however, performance of local attribute tasks may be reduced. |
| Author | Archambault, Daniel Purchase, Helen C. Pinaud, Bruno |
| Author_xml | – sequence: 1 givenname: Daniel surname: Archambault fullname: Archambault, Daniel organization: INRIA Bordeaux Sud-Ouest, France – sequence: 2 givenname: Helen C. surname: Purchase fullname: Purchase, Helen C. organization: University of Glasgow, United Kingdom – sequence: 3 givenname: Bruno surname: Pinaud fullname: Pinaud, Bruno organization: INRIA Bordeaux Sud-Ouest, France |
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| References_xml | – reference: Schaffer D., Zuo Z., Greenberg S., Bartram L., Dill I., Dubs S., Roseman M.: Navigating hierarchically clustered networks through fisheye and full-zoom methods. ACM Trans. on Computer-Human Interaction (TOCHI) 3, 2 (1996), 162-188. – reference: Van Ham F., Wattenberg M., Viegas F.: Mapping text with phrase nets. IEEE Trans. on Visualization and Computer Graphics 15, 6 (2009), 1169-1176. – reference: Di Giacomo E., Didimo W., Grilli L., Liotta G.: Graph visualization techniques for web clustering engines. IEEE Trans. on Visualization and Computer Graphics 13, 2 (MarchApril 2007), 294-304. – reference: Gansner E., Koren Y., North S.: Topological fish-eye views for visualizing large graphs. IEEE Trans. on Visualization and Computer Graphics 11, 4 (2005), 457-468. – reference: Eades P., Huang M. L.: Navigating clustered graphs using force-directed methods. Journal of Graph Algorithms and Applications 4, 3 (2000), 157-181. – reference: Abello I., Van Ham F., Krishnan N.: ASK-GraphView: A large scale graph visualization system. IEEE Trans. on Visualization and Computer Graphics (Proc. Vis/InfoVis '06) 12, 5 (2006), 669-676. – reference: Van Ham F., Rogowitz B. E.: Perceptual organisation in user-generated graph layouts. IEEE Trans. on Visualization and Computer Graphics (Proc. Vis/InfoVis '08) 14, 6 (2008), 1333-1339. – reference: Archambault D., Munzner T., Auber D.: GrouseFlocks: Steerable exploration of graph hierarchy space. IEEE Trans. on Visualization and Computer Graphics 14, 4 (2008), 900-913. – volume: 14 start-page: 1333 issue: 6 year: 2008 end-page: 1339 article-title: Perceptual organisation in user‐generated graph layouts publication-title: IEEE Trans. on Visualization and Computer Graphics (Proc. Vis/InfoVis '08) – volume: 14 start-page: 900 issue: 4 year: 2008 end-page: 913 article-title: GrouseFlocks: Steerable exploration of graph hierarchy space publication-title: IEEE Trans. on Visualization and Computer Graphics – start-page: 1 year: 2001 end-page: 10 – volume: 2 start-page: 162 year: 1996 end-page: 188 article-title: Navigating hierarchically clustered networks through fisheye and full‐zoom methods publication-title: ACM Trans. on Computer-Human Interaction (TOCHI) 3 – volume: 13 start-page: 294 issue: 2 year: MarchApril 2007 end-page: 304 article-title: Graph visualization techniques for web clustering engines publication-title: IEEE Trans. on Visualization and Computer Graphics – start-page: 1 year: 2008 end-page: 8 – start-page: 137 year: 2009 end-page: 144 – year: 2007 – volume: 15 start-page: 1169 issue: 6 year: 2009 end-page: 1176 article-title: Mapping text with phrase nets publication-title: IEEE Trans. on Visualization and Computer Graphics – start-page: 101 year: 1996 end-page: 112 – start-page: 87 year: 2009 end-page: 94 – year: 2000 – start-page: 199 year: 2004 end-page: 206 – start-page: 388 year: 1995 end-page: 403 – volume: 4 start-page: 157 issue: 3 year: 2000 end-page: 181 article-title: Navigating clustered graphs using force‐directed methods publication-title: Journal of Graph Algorithms and Applications – volume: 12 start-page: 669 issue: 5 year: 2006 end-page: 676 article-title: ASK‐GraphView: A large scale graph visualization system publication-title: IEEE Trans. on Visualization and Computer Graphics (Proc. Vis/InfoVis '06) – start-page: 199 year: 2006 end-page: 206 – start-page: 105 year: 2003 end-page: 126 – volume: 11 start-page: 457 issue: 4 year: 2005 end-page: 468 article-title: Topological fish‐eye views for visualizing large graphs publication-title: IEEE Trans. on Visualization and Computer Graphics – volume: 1353 start-page: 248 year: 1997 end-page: 261 – start-page: 811 year: 2006 end-page: 819 – ident: e_1_2_8_9_2 doi: 10.7155/jgaa.00029 – start-page: 101 volume-title: Proc. Graph Drawing (GD'96) year: 1996 ident: e_1_2_8_8_2 – ident: e_1_2_8_12_2 doi: 10.1145/1377966.1377970 – ident: e_1_2_8_5_2 doi: 10.1109/TVCG.2006.120 – ident: e_1_2_8_11_2 doi: 10.1109/TVCG.2005.66 – ident: e_1_2_8_6_2 – ident: e_1_2_8_3_2 – ident: e_1_2_8_13_2 doi: 10.1109/PACIFICVIS.2009.4906848 – ident: e_1_2_8_16_2 doi: 10.1007/3-540-63938-1_67 – ident: e_1_2_8_17_2 – ident: e_1_2_8_18_2 doi: 10.1145/230562.230577 – ident: e_1_2_8_22_2 doi: 10.1145/1124772.1124891 – ident: e_1_2_8_14_2 – ident: e_1_2_8_2_2 doi: 10.1109/TVCG.2008.34 – ident: e_1_2_8_15_2 – ident: e_1_2_8_7_2 doi: 10.1109/TVCG.2007.40 – ident: e_1_2_8_20_2 doi: 10.1109/INFVIS.2004.43 – start-page: 105 volume-title: Graph Drawing Software year: 2003 ident: e_1_2_8_4_2 – ident: e_1_2_8_21_2 doi: 10.1109/TVCG.2009.165 – ident: e_1_2_8_19_2 doi: 10.1109/TVCG.2008.155 – ident: e_1_2_8_10_2 doi: 10.1007/3-540-58950-3_393 |
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| Snippet | Graph visualization systems often exploit opaque metanodes to reduce visual clutter and improve the readability of large graphs. This filtering can be done in... AbstractGraph visualization systems often exploit opaque metanodes to reduce visual clutter and improve the readability of large graphs. This filtering can be... Graph visualization systems often exploit opaque metanodes to reduce visual clutter and improve the readability of large graphs. This filtering can be done in a... |
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| SubjectTerms | C (programming language) Clusters Clutter Computer graphics Computer Science Filtering Filtration G.2.2 [Discrete Mathematics]: Graph Theory-Graph Algorithms Graphs H.1.2 [Information Systems]: User/Machine Systems-Human Factors Other Readability Representations Studies Visual |
| Title | The Readability of Path-Preserving Clusterings of Graphs |
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