Visualization of Graph Data

Report
Visualization of Graph Data
CS 4390/5390 Data Visualization
Shirley Moore, Instructor
October 6, 2014
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Graphs and Trees
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graph – a set of nodes (vertices) connected by links (edges)
Links can be directed or undirected.
Two nodes are adjacent if they are connected by a link.
Two edges are adjacent if they share a common node.
Nodes and links can both have attributes.
A path from node a to node b is a sequence of adjacent edges from
a to b.
A cycle is a path that begins and ends at the same node.
A graph is connected if there exists a path between any two nodes.
A tree is a connected acyclic graph.
If there are n nodes, what is the maximum number of links
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in a directed graph?
in an undirected graph?
in a directed tree?
in an undirected tree?
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Graph Analytics
Slide courtesy of John Feo, PNNL
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Scientific Grids vs. Data Informatics Graphs
Slide courtesy of John Feo, PNNL
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Slide courtesy of Mathieu Bastian
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National Security
Slide courtesy of Mathieu Bastian
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Public Health
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Small Graphs
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Medium Graphs
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Large Graphs
• http://snap.stanford.edu/data/index.html
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Implicit vs. Explicit
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Graph Analytics
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Idiom Choices
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Triangular-vertical node-link layout
• What: Tree dataset
• Why: Hierarchical relationships, topology analysis tasks
• How: Vertical spatial position shows depth in tree, horizontal
spatial position is artifact of layout algorithm
• Scale: A few dozen nodes
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Spline-radial Layout
• What: tree dataset
• How:
– Depth encoded as distance from center of circle
– Links drawn as smoothly curving splines
– Reingold-Tilford layout algorithm
• Scale: A few hundred nodes
• Example written in D3:
– http://bl.ocks.org/mbostock/4063550
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D3 Tree Layout
• http://www.d3noob.org/2014/01/tree-diagrams-ind3js_11.html
• https://github.com/mbostock/d3/wiki/TreeLayout
• Representative of the D3 hierarchy layout
– https://github.com/mbostock/d3/wiki/HierarchyLayout
• Produces node-link diagrams of trees using the
Reingold-Tilford “tidy” algorithm
• Can input data that is in JSON (JavaScript Object
Notation) format
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Brainstorming Exercise 1
• How could we scale tree layouts to more than
a few hundred nodes?
– Possible strategy: use 3D
• Why or why not?
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Collapsible Tree Layout
• Example in D3
– http://bl.ocks.org/mbostock/4339083
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Treemap
Examples:
• http://bl.ocks.org/mbostock/4063582
• http://bost.ocks.org/mike/treemap/
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General Graph Layouts
• Also called network layouts
• Do not directly use spatial position to encode
attribute values
• Layout algorithms try to minimize number of
edge crossings and node overlaps.
• May use size and color encodings for node and
link attributes
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Force-Directed Placement
• Widely used for node-link network layout
• Position network elements according to a simulation of
physical forces – e.g.,
– Nodes push away from each other
– Links act like springs that draw their endpoints closer
• Can start by placing nodes randomly and iterating to
gradually improve layout
• Disadvantages
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Clusters may be artifacts of algorithm
Layout may be nondeterministic
May get stuck in local minimum energy configuration
Doesn’t scale past a few hundred nodes
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What-Why-How for
Force-Directed Placement
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Scalable Force-Directed Placement
(sfdp)
• Multilevel approach that transforms network into hierarchy of
successively simpler networks
• Algorithm: Layout coarsest network first, then improve layout
with more and more complex versions
• Examples: http://yifanhu.net/GALLERY/GRAPHS/index.html
• Graphviz software: http://www.graphviz.org/
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Adjacency Matrix View
Example: http://bost.ocks.org/mike/miserables/
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Characteristic Patterns in Node-link
and Matrix Views
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Brainstorming Exercise 2
• Which graph analysis tasks are better
supported by the node-link view, and which
are better supported by the matrix view?
• How does the above answer change with
increasing size of the graph?
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Graph Visualization Tools
• Sigma.js JavaScript library
• Gephi open source graph viz platform
• Many more!
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Preparation for Next Class
• Keep working with D3, use the tutorials on the
D3.js wiki
• Implement interaction in your parallel
coordinates visualization for Lab 3
• Decide which datasets to use for Lab 3
• Grad students and extra credit for undergrads:
Research k-means clustering
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Looking Ahead
• Quest (Quiz/Test) on Wed, Oct. 15
• Course exam on Wed, Nov. 19
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