Magnostics: Image-Based Search of Interesting Matrix Views for Guided Network Exploration

In this work we address the problem of retrieving potentially interesting matrix views to support the exploration of networks. We introduce Matrix Diagnostics (or Magnostics), following in spirit related approaches for rating and ranking other visualization techniques, such as Scagnostics for scatte...

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Bibliographic Details
Published in:	IEEE transactions on visualization and computer graphics Vol. 23; no. 1; pp. 31 - 40
Main Authors:	Behrisch, Michael, Bach, Benjamin, Hund, Michael, Delz, Michael, Von Ruden, Laura, Fekete, Jean-Daniel, Schreck, Tobias
Format:	Journal Article
Language:	English
Published:	United States IEEE 01-01-2017 The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Institute of Electrical and Electronics Engineers
Subjects:	Algorithms Computer Science Data analysis Data visualization Density measurement Feature Detection/Selection Feature extraction Human-Computer Interaction Layout Matrices (mathematics) Matrix Visualization Quality assessment Quality Metrics Relational Data Symmetric matrices Visual Quality Measures Visualization Index Terms—Matrix Visualization Relational Data Feature Detection/Selection Quality Metrics Visual Quality Measures
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Summary:	In this work we address the problem of retrieving potentially interesting matrix views to support the exploration of networks. We introduce Matrix Diagnostics (or Magnostics), following in spirit related approaches for rating and ranking other visualization techniques, such as Scagnostics for scatter plots. Our approach ranks matrix views according to the appearance of specific visual patterns, such as blocks and lines, indicating the existence of topological motifs in the data, such as clusters, bi-graphs, or central nodes. Magnostics can be used to analyze, query, or search for visually similar matrices in large collections, or to assess the quality of matrix reordering algorithms. While many feature descriptors for image analyzes exist, there is no evidence how they perform for detecting patterns in matrices. In order to make an informed choice of feature descriptors for matrix diagnostics, we evaluate 30 feature descriptors-27 existing ones and three new descriptors that we designed specifically for MAGNOSTICS-with respect to four criteria: pattern response, pattern variability, pattern sensibility, and pattern discrimination. We conclude with an informed set of six descriptors as most appropriate for Magnostics and demonstrate their application in two scenarios; exploring a large collection of matrices and analyzing temporal networks.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	1077-2626 1941-0506
DOI:	10.1109/TVCG.2016.2598467