Ch 9: Networks Papers: Sets, Stenomaps, TopoFisheye Tamara Munzner - PowerPoint PPT Presentation

Ch 9: Networks Papers: Sets, Stenomaps, TopoFisheye Tamara Munzner Department of Computer Science University of British Columbia CPSC 547, Information Visualization Day 9: 8 October 2015 http://www.cs.ubc.ca/~tmm/courses/547-15

Arrange networks and trees Arrange Networks and Trees Node–Link Diagrams Connection Marks NETWORKS TREES Adjacency Matrix Derived Table NETWORKS TREES Enclosure Containment Marks NETWORKS TREES 2

Idiom: force-directed placement • visual encoding – link connection marks, node point marks • considerations – spatial position: no meaning directly encoded • left free to minimize crossings – proximity semantics? • sometimes meaningful • sometimes arbitrary, artifact of layout algorithm • tension with length – long edges more visually salient than short • tasks – explore topology; locate paths, clusters • scalability – node/edge density E < 4N http://mbostock.github.com/d3/ex/force.html 3

Idiom: sfdp (multi-level force-directed placement) • data – original: network – derived: cluster hierarchy atop it • considerations – better algorithm for same encoding technique • same: fundamental use of space • hierarchy used for algorithm speed/quality but not shown explicitly [Efficient and high quality force-directed graph drawing. • (more on algorithm vs encoding in afternoon) Hu. The Mathematica Journal 10:37–71, 2005.] • scalability – nodes, edges: 1K-10K – hairball problem eventually hits 4 http://www.research.att.com/yifanhu/GALLERY/GRAPHS/index1.html

Idiom: adjacency matrix view • data: network – transform into same data/encoding as heatmap • derived data: table from network [NodeTrix: a Hybrid Visualization of Social Networks. Henry, Fekete, and McGuffin. IEEE TVCG (Proc. InfoVis) 13(6):1302-1309, 2007.] – 1 quant attrib • weighted edge between nodes – 2 categ attribs: node list x 2 • visual encoding – cell shows presence/absence of edge • scalability – 1K nodes, 1M edges [Points of view: Networks. Gehlenborg and Wong. Nature Methods 9:115.] 5

Connection vs. adjacency comparison • adjacency matrix strengths – predictability, scalability, supports reordering – some topology tasks trainable • node-link diagram strengths – topology understanding, path tracing – intuitive, no training needed http://www.michaelmcguffin.com/courses/vis/patternsInAdjacencyMatrix.png • empirical study – node-link best for small networks – matrix best for large networks • if tasks don’t involve topological structure! [On the readability of graphs using node-link and matrix-based representations: a controlled experiment and statistical analysis. Ghoniem, Fekete, and Castagliola. Information Visualization 4:2 (2005), 114–135.] 6

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Idiom: treemap • data – tree – 1 quant attrib at leaf nodes • encoding – area containment marks for hierarchical structure – rectilinear orientation – size encodes quant attrib • tasks http://tulip.labri.fr/Documentation/3_7/userHandbook/html/ch06.html – query attribute at leaf nodes • scalability – 1M leaf nodes 8