[PPT] - Chap 9:Arrange Networks Paper: Topological Fisheye Networks Tamara PowerPoint Presentation

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http://www.cs.ubc.ca/~tmm/courses/547-14/#chap9

Chap 9:Arrange Networks Paper: Topological Fisheye Networks

Tamara Munzner Department of Computer Science University of British Columbia

Information Visualization, CPSC 547 Oct 8 2014

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Arrange networks and trees

Node-link Diagrams Enclosure Adjacency Matrix

TREES NETWORKS

Connections and Marks

TREES NETWORKS

Derived Table

TREES NETWORKS

Containment Marks

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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

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http://mbostock.github.com/d3/ex/force.html

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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

(more on algorithm vs encoding in afternoon)
scalability

– nodes, edges: 1K-10K – hairball problem eventually hits

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[Efficient and high quality force-directed graph drawing. Hu. The Mathematica Journal 10:37–71, 2005.]

http://www.research.att.com/yifanhu/GALLERY/GRAPHS/index1.html

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Idiom: adjacency matrix view

data: network

– transform into same data/encoding as heatmap

derived data: table from network

– 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

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[NodeTrix: a Hybrid Visualization of Social Networks. Henry, Fekete, and McGuffin. IEEE TVCG (Proc. InfoVis) 13(6):1302-1309, 2007.] [Points of view: Networks. Gehlenborg and

Wong. Nature Methods 9:115.]

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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

empirical study

– node-link best for small networks – matrix best for large networks

if tasks don’t involve topological structure!

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[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.]

http://www.michaelmcguffin.com/courses/vis/patternsInAdjacencyMatrix.png

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Idiom: radial node-link tree

data

– tree

encoding

– link connection marks – point node marks – radial axis orientation

angular proximity: siblings
distance from center: depth in tree
tasks

– understanding topology, following paths

scalability

– 1K - 10K nodes

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http://mbostock.github.com/d3/ex/tree.html

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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

– query attribute at leaf nodes

scalability

– 1M leaf nodes

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http://tulip.labri.fr/Documentation/3_7/userHandbook/html/ch06.html

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Link marks: Connection and Containment

marks as links (vs. nodes)

– common case in network drawing – 1D case: connection

ex: all node-link diagrams
emphasizes topology, path tracing
networks and trees

– 2D case: containment

ex: all treemap variants
emphasizes attribute values at leaves (size coding)
only trees

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Node–Link Diagram Treemap

[Elastic Hierarchies: Combining Treemaps and Node-Link

Diagrams. Dong, McGuffin, and Chignell. Proc. InfoVis

2005, p. 57-64.]

Containment Connection

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Tree drawing idioms comparison

data shown

– link relationships – tree depth – sibling order

design choices

– connection vs containment link marks – rectilinear vs radial layout – spatial position channels

considerations

– redundant? arbitrary? – information density?

avoid wasting space

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[Quantifying the Space-Efficiency of 2D Graphical Representations of

Trees. McGuffin and Robert. Information

Visualization 9:2 (2010), 115–140.]

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Idiom: GrouseFlocks

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data: compound graphs

– network – cluster hierarchy atop it

derived or interactively chosen
visual encoding

– connection marks for network links – containment marks for hierarchy – point marks for nodes

dynamic interaction

– select individual metanodes in hierarchy to expand/ contract

[GrouseFlocks: Steerable Exploration of Graph Hierarchy Space. Archambault, Munzner, and Auber. IEEE TVCG 14(4): 900-913, 2008.] Graph Hierarchy 1

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Topological Fisheye Views

derived data

– input: laid-out network (spatial positions for nodes) – output: multilevel hierarchy from graph coarsening

interaction

– user changed selected focus point

visual encoding

– hybrid view made from cut through several hierarchy levels

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[Fig 4,7. Topological Fisheye Views for Visualizing Large Graphs. Gansner, Koren and North, IEEE TVCG 11(4), p 457-468, 2005]

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Topological Fisheye Views

derived data

– input: laid-out network (spatial positions for nodes) – output: multilevel hierarchy from graph coarsening

interaction

– user changed selected focus point

visual encoding

– hybrid view made from cut through several hierarchy levels

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[Fig 4,8. Topological Fisheye Views for Visualizing Large Graphs. Gansner, Koren and North, IEEE TVCG 11(4), p 457-468, 2005]

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Coarsening requirements

uniform cluster/metanode size
match coarse and fine layout geometries
scalable

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[Fig 3. Topological Fisheye Views for Visualizing Large Graphs. Gansner, Koren and North, IEEE TVCG 11(4), p 457-468, 2005]

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Coarsening strategy

must preserve graph-theoretic properties
use both topology and geometry

– topological distance (hops away) – geometric distance - but not just proximity alone!

just contracting nodes/edges could create new cycles
derived data: proximity graph

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[Fig 10, 12. Topological Fisheye Views for Visualizing Large

Graphs. Gansner, Koren and

North, IEEE TVCG 11(4), p 457-468, 2005]

what not to do!

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Candidate pairs: neighbors in original and proximity graph

proximity graph: compromise between larger DT and smaller RNG

– better than original graph neighbors alone

slow for cases like star graph
maximize weighted sum of

– geometric proximity

goal: preserve geometry

– cluster size

goal: keep uniform cluster size

– normalized connection strength

goal: preserve topology

– neighborhood similarity

goal: preserve topology

– degree

goal: penalize high-degree nodes to avoid salient artifacts and computational problems

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Hybrid graph creation

cut through coarsening hierarchy to get active nodes

– animated transitions between states

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[Fig 10, 12. Topological Fisheye Views for Visualizing Large

Graphs. Gansner, Koren and

North, IEEE TVCG 11(4), p 457-468, 2005]

SLIDE 19

Final distortion

geometric distortion for uniform density
(colorcoded by hierarchy depth just to illustrate algorithm)

– compare to original – compare to simple topologically unaware fisheye distortion

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[Fig 2,15. Topological Fisheye Views for Visualizing Large Graphs. Gansner, Koren and North, IEEE TVCG 11(4), p 457-468, 2005]

Chap 9:Arrange Networks Paper: Topological Fisheye Networks Tamara - - PowerPoint PPT Presentation

Chap 9:Arrange Networks Paper: Topological Fisheye Networks

Tamara Munzner Department of Computer Science University of British Columbia

Arrange networks and trees

Idiom: force-directed placement

Idiom: sfdp (multi-level force-directed placement)

Idiom: adjacency matrix view

Connection vs. adjacency comparison

Idiom: radial node-link tree

Idiom: treemap

Link marks: Connection and Containment

Tree drawing idioms comparison

Idiom: GrouseFlocks

Further reading

Topological Fisheye Views

Topological Fisheye Views

Coarsening requirements

Coarsening strategy

Candidate pairs: neighbors in original and proximity graph

Hybrid graph creation

Final distortion