Visual Analysis of Sets of Heterogeneous Matrices Using Projection-Based Distance Functions and Semantic Zoom
Matrix visualization is an established technique in the analysis of relational data. It is applicable to large, dense networks, where node‐link representations may not be effective. Recently, domains have emerged in which the comparative analysis of sets of matrices of potentially varying size is re...
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| Published in | Computer graphics forum Vol. 33; no. 3; pp. 411 - 420 |
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| Main Authors | , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Oxford
Blackwell Publishing Ltd
01.06.2014
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| Subjects | |
| Online Access | Get full text |
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| Abstract | Matrix visualization is an established technique in the analysis of relational data. It is applicable to large, dense networks, where node‐link representations may not be effective. Recently, domains have emerged in which the comparative analysis of sets of matrices of potentially varying size is relevant. For example, to monitor computer network traffic a dynamic set of hosts and their peer‐to‐peer connections on different ports must be analysed. A matrix visualization focused on the display of one matrix at a time cannot cope with this task.
We address the research problem of the visual analysis of sets of matrices. We present a technique for comparing matrices of potentially varying size. Our approach considers the rows and/or columns of a matrix as the basic elements of the analysis. We project these vectors for pairs of matrices into a low‐dimensional space which is used as the reference to compare matrices and identify relationships among them. Bipartite graph matching is applied on the projected elements to compute a measure of distance. A key advantage of this measure is that it can be interpreted and manipulated as a visual distance function, and serves as a comprehensible basis for ranking, clustering and comparison in sets of matrices. We present an interactive system in which users may explore the matrix distances and understand potential differences in a set of matrices. A flexible semantic zoom mechanism enables users to navigate through sets of matrices and identify patterns at different levels of detail. We demonstrate the effectiveness of our approach through a case study and provide a technical evaluation to illustrate its strengths. |
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| AbstractList | Matrix visualization is an established technique in the analysis of relational data. It is applicable to large, dense networks, where node-link representations may not be effective. Recently, domains have emerged in which the comparative analysis of sets of matrices of potentially varying size is relevant. For example, to monitor computer network traffic a dynamic set of hosts and their peer-to-peer connections on different ports must be analysed. A matrix visualization focused on the display of one matrix at a time cannot cope with this task. We address the research problem of the visual analysis of sets of matrices. We present a technique for comparing matrices of potentially varying size. Our approach considers the rows and/or columns of a matrix as the basic elements of the analysis. We project these vectors for pairs of matrices into a low-dimensional space which is used as the reference to compare matrices and identify relationships among them. Bipartite graph matching is applied on the projected elements to compute a measure of distance. A key advantage of this measure is that it can be interpreted and manipulated as a visual distance function, and serves as a comprehensible basis for ranking, clustering and comparison in sets of matrices. We present an interactive system in which users may explore the matrix distances and understand potential differences in a set of matrices. A flexible semantic zoom mechanism enables users to navigate through sets of matrices and identify patterns at different levels of detail. We demonstrate the effectiveness of our approach through a case study and provide a technical evaluation to illustrate its strengths. Matrix visualization is an established technique in the analysis of relational data. It is applicable to large, dense networks, where node-link representations may not be effective. Recently, domains have emerged in which the comparative analysis of sets of matrices of potentially varying size is relevant. For example, to monitor computer network traffic a dynamic set of hosts and their peer-to-peer connections on different ports must be analysed. A matrix visualization focused on the display of one matrix at a time cannot cope with this task. We address the research problem of the visual analysis of sets of matrices. We present a technique for comparing matrices of potentially varying size. Our approach considers the rows and/or columns of a matrix as the basic elements of the analysis. We project these vectors for pairs of matrices into a low-dimensional space which is used as the reference to compare matrices and identify relationships among them. Bipartite graph matching is applied on the projected elements to compute a measure of distance. A key advantage of this measure is that it can be interpreted and manipulated as a visual distance function, and serves as a comprehensible basis for ranking, clustering and comparison in sets of matrices. We present an interactive system in which users may explore the matrix distances and understand potential differences in a set of matrices. A flexible semantic zoom mechanism enables users to navigate through sets of matrices and identify patterns at different levels of detail. We demonstrate the effectiveness of our approach through a case study and provide a technical evaluation to illustrate its strengths. [PUBLICATION ABSTRACT] Matrix visualization is an established technique in the analysis of relational data. It is applicable to large, dense networks, where node‐link representations may not be effective. Recently, domains have emerged in which the comparative analysis of sets of matrices of potentially varying size is relevant. For example, to monitor computer network traffic a dynamic set of hosts and their peer‐to‐peer connections on different ports must be analysed. A matrix visualization focused on the display of one matrix at a time cannot cope with this task. We address the research problem of the visual analysis of sets of matrices. We present a technique for comparing matrices of potentially varying size. Our approach considers the rows and/or columns of a matrix as the basic elements of the analysis. We project these vectors for pairs of matrices into a low‐dimensional space which is used as the reference to compare matrices and identify relationships among them. Bipartite graph matching is applied on the projected elements to compute a measure of distance. A key advantage of this measure is that it can be interpreted and manipulated as a visual distance function, and serves as a comprehensible basis for ranking, clustering and comparison in sets of matrices. We present an interactive system in which users may explore the matrix distances and understand potential differences in a set of matrices. A flexible semantic zoom mechanism enables users to navigate through sets of matrices and identify patterns at different levels of detail. We demonstrate the effectiveness of our approach through a case study and provide a technical evaluation to illustrate its strengths. |
| Author | Fischer, Fabian Thonnard, Olivier Keim, Daniel Schreck, Tobias Behrisch, Michael Davey, James Kohlhammer, Jörn |
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| Cites_doi | 10.1109/TVCG.2013.192 10.1109/34.601251 10.1080/00029890.1962.11989827 10.1186/1471-2105-11-330 10.1515/9783110854688 10.1002/0470013192.bsa501 10.1109/34.6778 10.1007/3-540-70659-3_13 10.1109/T-C.1973.223602 10.1109/TVCG.2012.191 10.1145/996546.996554 10.1023/B:VISI.0000029664.99615.94 10.1109/PACIFICVIS.2008.4475479 10.1057/palgrave.ivs.9500092 10.1201/9781420036121 10.1109/TSMC.1983.6313167 10.1109/TPAMI.2004.1265866 10.1142/S0129054196000051 10.1145/1345448.1345453 10.1007/978-3-540-33037-0_26 10.1109/TVCG.2012.208 10.1109/VAST.2012.6400549 |
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| Copyright | 2014 The Author(s) Computer Graphics Forum © 2014 The Eurographics Association and John Wiley & Sons Ltd. Published by John Wiley & Sons Ltd. 2014 The Eurographics Association and John Wiley & Sons Ltd. |
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| References | van der Maaten L., Hinton G.: Visualizing Data using t-SNE. JMLR 9 (Nov. 2008), 2579-2605. 5 Zhang K., Wang J. T.-L., Shasha D.: On the editing distance between undirected acyclic graphs. Int. J. Found. Comput. Sci. 7, 1 (1996), 43-58. 3 Bertin J.: Graphics and graphic information-processing. de Gruyter, 1981. 1 Burch M., Vehlow C., Beck F., Diehl S., Weiskopf D.: Parallel edge splatting for scalable dynamic graph vis. IEEE TVCG 17, 12 (2011), 2344-2353. 3 Gusfield D., Irving R.W.: The stable marriage problem: structure and algorithms. MIT Press, 1989. 5 Cordella L., Foggia P., Sansone C., Vento M.: An efficient algorithm for the inexact matching of arg graphs using a contextual transformational model. In Pattern Recognition, 1996., Proc. of the 13th Int. Conference on (1996), vol. 3, IEEE, pp. 180-184 vol. 3. 3 Jolliffe I.: Principal component analysis. Wiley Online Library, 2005. 5 Lowe D.G.: Distinctive image features from scale-invariant keypoints. IJCV 60, 2 (2004), 91-110. 3 Dinkla K., Westenberg M., van Wijk J.: Compressed adjacency matrices: Untangling gene regulatory networks. Vis. and Computer Graphics, IEEE Trans. on 18, 12 (2012), 2457-2466. 2 Ghoniem M., Fekete J.-D., Castagliola P.: On the readability of graphs using node-link and matrix-based representations: a controlled experiment and statistical analysis. Info Vis. 4, 2 (July 2005), 114-135. 1, 2 Cox T.F., Cox M.: Multidimensional Scaling, Second edition, 2 ed. Chapman and Hall/CRC, 2000. 5 Henry N., Fekete J.-D.: Matrixexplorer: a dual-representation system to explore social networks. IEEE TVCG 12 (2006), 677-684. 1, 2 Yang D., Xie Z., Rundensteiner E.A., Ward M.O.: Managing discoveries in the visual analytics process. SIGKDD Explor. Newsl. 9, 2 (Dec. 2007), 22-29. 3 Caelli T., Kosinov S.: An eigenspace projection clustering method for inexact graph matching. IEEE TPAMI 26, 4 (2004), 515-519. 3, 4, 9 Pelillo M.: A unifying framework for relational structure matching. In Pattern Recognition, 1998. Proc.. Fourteenth Int. Conference on (1998), vol. 2, pp. 1316-1319 vol. 2. 3 Gale D., Shapley L.S.: College admissions and the stability of marriage. The American Mathematical Monthly 69, 1 (1962), pp. 9-15. 5 Petit J.: Experiments on the minimum linear arrangement problem. J. Exp. Algorithmics 8 (Dec. 2003). 8 Fischler M.A., Elschlager R.: The representation and matching of pictorial structures. IEEE TC 22, 1 (1973), 67-92. 3 Umeyama S.: An eigendecomposition approach to weighted graph matching problems. Pattern Analysis and Machine Intelligence, IEEE Trans. on 10, 5 (1988), 695-703. 3 Wilson R., Hancock E.: Structural matching by discrete relaxation. Pattern Analysis and Machine Intelligence, IEEE Trans. on 19, 6 (1997), 634-648. 3 Sanfeliu A., Fu K.-S.: A distance measure between attributed relational graphs for pattern recognition. Systems, Man and Cybernetics, IEEE Trans. on, 3 (1983), 353-362. 3 Wu H.-M., Tzeng S., Chen C.-H.: Handbook of Data Vis. Springer, 2008, ch. Matrix Visualization, pp. 681-708. 2 Carreira-Perpinan M.A.: A Review of Dimension Reduction Techniques. Department of Computer Science. University of Sheffield. Tech. Rep. CS-96-09 09 (1997), 1-69. 5 Sips M., Köthur P., Unger A., Hege H.-C., Dransch D.: A visual analytics approach to multiscale exploration of environmental time series. IEEE Trans. Vis. Comput. Graph. 18, 12 (2012), 2899-2907. 2 Perin C., Vuillemot R., Fekete J.-D.: Soccerstories: A kick-off for visual soccer analysis. Vis. and Computer Graphics, IEEE Trans. on 19, 12 (2013), 2506-2515. 8 Henry N., Fekete J.-D., McGuffin M.J.: NodeTrix: a Hybrid Visualization of Social Networks. IEEE TVCG 13, 6 (2007), 1302-9. 1, 2 2002; 2396 2004; 60 2012 2006; 12 1983; 3 2011 2010 2004; 26 1988; 10 2008; 9 2009 2008 2007 2012; 18 2005 2004 2004; 1 1997; 09 2002 2011; 17 2007; 13 2013; 19 1973; 22 2000 2003; 8 1997; 19 2005; 4 2007; 9 1962; 69 1998; 2 1981 2013 1996; 3 1989 1996; 7 e_1_2_10_22_2 e_1_2_10_23_2 Burch M. (e_1_2_10_7_2) 2011; 17 Abello J. (e_1_2_10_2_2) 2004 e_1_2_10_40_2 (e_1_2_10_33_2) 2008; 9 Carreira‐Perpinan M.A. (e_1_2_10_12_2) 1997; 09 Wyk B. (e_1_2_10_38_2) 2002 Henry N. (e_1_2_10_21_2) 2007; 13 e_1_2_10_19_2 e_1_2_10_3_2 e_1_2_10_17_2 e_1_2_10_39_2 e_1_2_10_15_2 e_1_2_10_4_2 e_1_2_10_37_2 e_1_2_10_13_2 e_1_2_10_36_2 e_1_2_10_6_2 e_1_2_10_14_2 e_1_2_10_35_2 e_1_2_10_11_2 e_1_2_10_8_2 (e_1_2_10_34_2) 2009 e_1_2_10_32_2 e_1_2_10_10_2 e_1_2_10_31_2 e_1_2_10_30_2 Fekete J.‐D. (e_1_2_10_16_2) 2004 Behrisch M. (e_1_2_10_5_2) 2012 Paulovich F.V. (e_1_2_10_28_2) 2007 Gusfield D. (e_1_2_10_18_2) 1989 Henry N. (e_1_2_10_20_2) 2006; 12 Pelillo M. (e_1_2_10_26_2) 1998; 2 e_1_2_10_29_2 Cordella L. (e_1_2_10_9_2) 1996; 3 e_1_2_10_27_2 e_1_2_10_24_2 e_1_2_10_25_2 |
| References_xml | – reference: Bertin J.: Graphics and graphic information-processing. de Gruyter, 1981. 1 – reference: Cox T.F., Cox M.: Multidimensional Scaling, Second edition, 2 ed. Chapman and Hall/CRC, 2000. 5 – reference: Yang D., Xie Z., Rundensteiner E.A., Ward M.O.: Managing discoveries in the visual analytics process. SIGKDD Explor. Newsl. 9, 2 (Dec. 2007), 22-29. 3 – reference: Dinkla K., Westenberg M., van Wijk J.: Compressed adjacency matrices: Untangling gene regulatory networks. Vis. and Computer Graphics, IEEE Trans. on 18, 12 (2012), 2457-2466. 2 – reference: Sips M., Köthur P., Unger A., Hege H.-C., Dransch D.: A visual analytics approach to multiscale exploration of environmental time series. IEEE Trans. Vis. Comput. Graph. 18, 12 (2012), 2899-2907. 2 – reference: Lowe D.G.: Distinctive image features from scale-invariant keypoints. IJCV 60, 2 (2004), 91-110. 3 – reference: Sanfeliu A., Fu K.-S.: A distance measure between attributed relational graphs for pattern recognition. Systems, Man and Cybernetics, IEEE Trans. on, 3 (1983), 353-362. 3 – reference: Petit J.: Experiments on the minimum linear arrangement problem. J. Exp. Algorithmics 8 (Dec. 2003). 8 – reference: Fischler M.A., Elschlager R.: The representation and matching of pictorial structures. IEEE TC 22, 1 (1973), 67-92. 3 – reference: Umeyama S.: An eigendecomposition approach to weighted graph matching problems. Pattern Analysis and Machine Intelligence, IEEE Trans. on 10, 5 (1988), 695-703. 3 – reference: Pelillo M.: A unifying framework for relational structure matching. In Pattern Recognition, 1998. Proc.. Fourteenth Int. Conference on (1998), vol. 2, pp. 1316-1319 vol. 2. 3 – reference: Wilson R., Hancock E.: Structural matching by discrete relaxation. Pattern Analysis and Machine Intelligence, IEEE Trans. on 19, 6 (1997), 634-648. 3 – reference: Ghoniem M., Fekete J.-D., Castagliola P.: On the readability of graphs using node-link and matrix-based representations: a controlled experiment and statistical analysis. Info Vis. 4, 2 (July 2005), 114-135. 1, 2 – reference: Wu H.-M., Tzeng S., Chen C.-H.: Handbook of Data Vis. Springer, 2008, ch. Matrix Visualization, pp. 681-708. 2 – reference: Henry N., Fekete J.-D.: Matrixexplorer: a dual-representation system to explore social networks. IEEE TVCG 12 (2006), 677-684. 1, 2 – reference: Gale D., Shapley L.S.: College admissions and the stability of marriage. The American Mathematical Monthly 69, 1 (1962), pp. 9-15. 5 – reference: Cordella L., Foggia P., Sansone C., Vento M.: An efficient algorithm for the inexact matching of arg graphs using a contextual transformational model. In Pattern Recognition, 1996., Proc. of the 13th Int. Conference on (1996), vol. 3, IEEE, pp. 180-184 vol. 3. 3 – reference: van der Maaten L., Hinton G.: Visualizing Data using t-SNE. JMLR 9 (Nov. 2008), 2579-2605. 5 – reference: Jolliffe I.: Principal component analysis. Wiley Online Library, 2005. 5 – reference: Zhang K., Wang J. T.-L., Shasha D.: On the editing distance between undirected acyclic graphs. Int. J. Found. Comput. Sci. 7, 1 (1996), 43-58. 3 – reference: Henry N., Fekete J.-D., McGuffin M.J.: NodeTrix: a Hybrid Visualization of Social Networks. IEEE TVCG 13, 6 (2007), 1302-9. 1, 2 – reference: Burch M., Vehlow C., Beck F., Diehl S., Weiskopf D.: Parallel edge splatting for scalable dynamic graph vis. IEEE TVCG 17, 12 (2011), 2344-2353. 3 – reference: Gusfield D., Irving R.W.: The stable marriage problem: structure and algorithms. MIT Press, 1989. 5 – reference: Carreira-Perpinan M.A.: A Review of Dimension Reduction Techniques. Department of Computer Science. University of Sheffield. Tech. Rep. CS-96-09 09 (1997), 1-69. 5 – reference: Perin C., Vuillemot R., Fekete J.-D.: Soccerstories: A kick-off for visual soccer analysis. Vis. and Computer Graphics, IEEE Trans. on 19, 12 (2013), 2506-2515. 8 – reference: Caelli T., Kosinov S.: An eigenspace projection clustering method for inexact graph matching. IEEE TPAMI 26, 4 (2004), 515-519. 3, 4, 9 – start-page: 61 year: 2012 end-page: 65 – year: 2011 – year: 1981 – volume: 12 start-page: 677 year: 2006 end-page: 684 article-title: Matrixexplorer: a dual‐representation system to explore social networks publication-title: IEEE TVCG – volume: 17 start-page: 2344 issue: 12 year: 2011 end-page: 2353 article-title: Parallel edge splatting for scalable dynamic graph vis publication-title: IEEE TVCG – volume: 13 start-page: 1302 issue: 6 year: 2007 end-page: 9 article-title: N T : a Hybrid Visualization of Social Networks publication-title: IEEE TVCG – year: 2005 – volume: 19 start-page: 634 issue: 6 year: 1997 end-page: 648 article-title: Structural matching by discrete relaxation publication-title: Pattern Analysis and Machine Intelligence, IEEE Trans. on – volume: 3 start-page: 353 year: 1983 end-page: 362 article-title: A distance measure between attributed relational graphs for pattern recognition publication-title: Systems, Man and Cybernetics, IEEE Trans. on – year: 1989 – start-page: 133 year: 2002 end-page: 142 – volume: 3 start-page: 180 year: 1996 end-page: 184 article-title: An efficient algorithm for the inexact matching of arg graphs using a contextual transformational model publication-title: Pattern Recognition – year: 2000 – volume: 9 start-page: 22 issue: 2 year: 2007 end-page: 29 article-title: Managing discoveries in the visual analytics process publication-title: SIGKDD Explor. Newsl. – start-page: 27 year: 2007 end-page: 36 – start-page: 681 year: 2008 end-page: 708 – volume: 69 start-page: 9 issue: 1 year: 1962 end-page: 15 article-title: College admissions and the stability of marriage publication-title: The American Mathematical Monthly – start-page: 155 year: 2009 end-page: 162 – volume: 8 year: 2003 article-title: Experiments on the minimum linear arrangement problem publication-title: J. Exp. Algorithmics – volume: 2396 start-page: 74 year: 2002 end-page: 83 – volume: 19 start-page: 2506 issue: 12 year: 2013 end-page: 2515 article-title: S : A kick‐off for visual soccer analysis publication-title: Vis. and Computer Graphics, IEEE Trans. on – year: 2010 – year: 2012 – volume: 09 start-page: 1 year: 1997 end-page: 69 article-title: A Review of Dimension Reduction Techniques publication-title: Department of Computer Science. University of Sheffield. Tech. Rep. CS-96-09 – start-page: 215 year: 2008 end-page: 222 – start-page: 167 year: 2004 end-page: 174 – volume: 22 start-page: 67 issue: 1 year: 1973 end-page: 92 article-title: The representation and matching of pictorial structures publication-title: IEEE TC – volume: 26 start-page: 515 issue: 4 year: 2004 end-page: 519 article-title: An eigenspace projection clustering method for inexact graph matching publication-title: IEEE TPAMI – volume: 7 start-page: 43 issue: 1 year: 1996 end-page: 58 article-title: On the editing distance between undirected acyclic graphs publication-title: Int. J. Found. Comput. Sci. – volume: 2 start-page: 1316 issue: 3 year: 1998 end-page: 1319 article-title: A unifying framework for relational structure matching publication-title: Pattern Recognition – volume: 9 start-page: 2579 year: 2008 end-page: 2605 article-title: Visualizing Data using t‐SNE publication-title: JMLR – volume: 60 start-page: 91 issue: 2 year: 2004 end-page: 110 article-title: Distinctive image features from scale‐invariant keypoints publication-title: IJCV – volume: 18 start-page: 2457 issue: 12 year: 2012 end-page: 2466 article-title: Compressed adjacency matrices: Untangling gene regulatory networks publication-title: Vis. and Computer Graphics, IEEE Trans. on – volume: 4 start-page: 114 issue: 2 year: 2005 end-page: 135 article-title: On the readability of graphs using node‐link and matrix‐based representations: a controlled experiment and statistical analysis publication-title: Info Vis. – volume: 1 start-page: 183 year: 2004 end-page: 190 – volume: 18 start-page: 2899 issue: 12 year: 2012 end-page: 2907 article-title: A visual analytics approach to multiscale exploration of environmental time series publication-title: IEEE Trans. Vis. Comput. Graph. – volume: 10 start-page: 695 issue: 5 year: 1988 end-page: 703 article-title: An eigendecomposition approach to weighted graph matching problems publication-title: Pattern Analysis and Machine Intelligence, IEEE Trans. on – year: 2013 – ident: e_1_2_10_23_2 – ident: e_1_2_10_29_2 doi: 10.1109/TVCG.2013.192 – ident: e_1_2_10_36_2 doi: 10.1109/34.601251 – ident: e_1_2_10_19_2 doi: 10.1080/00029890.1962.11989827 – start-page: 74 volume-title: Lecture Notes in Computer Science year: 2002 ident: e_1_2_10_38_2 – start-page: 61 volume-title: Proc. EuroVA International Workshop on Visual Analytics year: 2012 ident: e_1_2_10_5_2 – ident: e_1_2_10_6_2 doi: 10.1186/1471-2105-11-330 – ident: e_1_2_10_4_2 doi: 10.1515/9783110854688 – start-page: 167 volume-title: Proc. of the IEEE Symp. on Info Vis year: 2004 ident: e_1_2_10_16_2 – ident: e_1_2_10_22_2 doi: 10.1002/0470013192.bsa501 – ident: e_1_2_10_32_2 doi: 10.1109/34.6778 – volume-title: The stable marriage problem: structure and algorithms year: 1989 ident: e_1_2_10_18_2 – ident: e_1_2_10_24_2 doi: 10.1007/3-540-70659-3_13 – ident: e_1_2_10_15_2 doi: 10.1109/T-C.1973.223602 – ident: e_1_2_10_31_2 doi: 10.1109/TVCG.2012.191 – start-page: 27 volume-title: Proc. of the 2008 Brazilian SIBGRAPI year: 2007 ident: e_1_2_10_28_2 – volume: 9 start-page: 2579 year: 2008 ident: e_1_2_10_33_2 article-title: Visualizing Data using t‐SNE publication-title: JMLR – ident: e_1_2_10_27_2 doi: 10.1145/996546.996554 – ident: e_1_2_10_25_2 doi: 10.1023/B:VISI.0000029664.99615.94 – volume: 09 start-page: 1 year: 1997 ident: e_1_2_10_12_2 article-title: A Review of Dimension Reduction Techniques publication-title: Department of Computer Science. University of Sheffield. Tech. Rep. CS-96-09 – ident: e_1_2_10_14_2 doi: 10.1109/PACIFICVIS.2008.4475479 – volume: 12 start-page: 677 year: 2006 ident: e_1_2_10_20_2 article-title: Matrixexplorer: a dual‐representation system to explore social networks publication-title: IEEE TVCG – volume: 2 start-page: 1316 issue: 3 year: 1998 ident: e_1_2_10_26_2 article-title: A unifying framework for relational structure matching publication-title: Pattern Recognition – volume: 3 start-page: 180 year: 1996 ident: e_1_2_10_9_2 article-title: An efficient algorithm for the inexact matching of arg graphs using a contextual transformational model publication-title: Pattern Recognition – ident: e_1_2_10_17_2 doi: 10.1057/palgrave.ivs.9500092 – ident: e_1_2_10_10_2 – ident: e_1_2_10_8_2 doi: 10.1201/9781420036121 – ident: e_1_2_10_30_2 doi: 10.1109/TSMC.1983.6313167 – volume: 13 start-page: 1302 issue: 6 year: 2007 ident: e_1_2_10_21_2 article-title: NodeTrix: a Hybrid Visualization of Social Networks publication-title: IEEE TVCG – ident: e_1_2_10_11_2 doi: 10.1109/TPAMI.2004.1265866 – ident: e_1_2_10_40_2 doi: 10.1142/S0129054196000051 – ident: e_1_2_10_35_2 – ident: e_1_2_10_39_2 doi: 10.1145/1345448.1345453 – volume: 17 start-page: 2344 issue: 12 year: 2011 ident: e_1_2_10_7_2 article-title: Parallel edge splatting for scalable dynamic graph vis publication-title: IEEE TVCG – ident: e_1_2_10_37_2 doi: 10.1007/978-3-540-33037-0_26 – ident: e_1_2_10_13_2 doi: 10.1109/TVCG.2012.208 – start-page: 155 volume-title: Proc. IEEE Symp. on VAST year: 2009 ident: e_1_2_10_34_2 – start-page: 183 volume-title: Info Vis., 2004. INFOVIS 2004. IEEE Symp. on year: 2004 ident: e_1_2_10_2_2 – ident: e_1_2_10_3_2 doi: 10.1109/VAST.2012.6400549 |
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| SubjectTerms | Analysis Categories and Subject Descriptors (according to ACM CCS) Computer networks H.3.3 [Information Storage and Retrieval]: Information Search and Retrieval-Search process Information processing Mathematical analysis Mathematical models Monitors Networks Semantics Studies Visual Visualization |
| Title | Visual Analysis of Sets of Heterogeneous Matrices Using Projection-Based Distance Functions and Semantic Zoom |
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