1. Analyzing, Visualizing
Noordin Top Terror Network
(Gephi)
University of Minnesota Outreach (UCINET Net Draw)
Tools of the Trade
When pen and paper (or even Excel) just won’t do
Infantry Training Task Network (iGraph)
Infantry Training Task Network (ORA)
US Power Grid (Pajek)
CDT Maldonado, CDT Talty, MAJ Koban
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2. Why?
We may not need to use analytical packages and visualization for graph theory.
What are some differences between graphs and networks?
To do real work with networks, complex networks, dynamic networks, you need to get comfortable with software.
Analysis
Visualization…I picture is worth a 1000 words…or more.
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3. You’ve Got MANY Options
In fact, more than 50 from just the following two sites:
University of Gronigen:
Good ol’ Wikipedia:
We’re going to discuss the following:
UCINET (NetDraw)
Python (NetworkX)
R (iGraph)
ORA
Mathematica
Pajek
Gephi
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4. UCINET (NetDraw)
You can get it here:
Lots of history (been around since 1987), widely used in academia
Actively maintained (2-day workshop hosted by Borgatti in April)
Additional information about NetDraw in Chapter 4 of
Pro:
Flexible data import
Many metrics
Supports more complex network analysis
Compatible with many visualization platforms
Con:
Steeper learning curve
Online help is “sophisticated.”
Difficult to filter data being viewed
NetDraw visualization relatively poor, cannot format output.
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5. Pajek You can get it here: http://mrvar.fdv.uni-lj.si/pajek/
Lots of history (been around since 1996)
No maintenance since May 2016 (active prior to that)
Helpful book at
Offers both Pajek (good for large networks) and Pajek XXL (good for huge networks).
Pro:
Good online documentation
Many metrics
Supports more complex network analysis
Con:
Steeper learning curve
Primarily for complex mappings of very large networks
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6. Python (NetworkX) You can get it here: http://networkx.github.io/
If you like Python and networks, you’ll love NetworkX!
Pro:
Written completely in Python
Good online documentation
Many metrics
Supports more complex network analysis
Algorithms are easy to find and manipulate
Con:
May have poor performance and/or memory usage, depending on which algorithms are being called (do to dictionary data structure).
Outputs are relatively simplistic and difficult to manipulate
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7. R (iGraph) You can get it here: http://igraph.org/r/ …
If you like R and networks, you’ll love iGraph!
## Download and install the package install.packages("igraph") ## Load package library(igraph)
Pro:
Many data analysis features
Standard graph statistics are present
Faster than NetworkX (>>)
Con:
API takes time to get used to (for Python users).
Tools designed for analysts by analysts
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8. ORA ORA: Organizational Risk Analyzer
You can get it here:
Or here:
Used in TRADOC’s Advanced Network Analysis and Targeting (ANAT) course
Pro:
Very low barrier to entry.
Dynamic and temporal network analysis
Advanced network analysis
Geo-spatial analysis tools.
Con:
Costs to use any functionality beyond ORA Lite ($480 per copy)
Visualization is not as impressive as other platforms
Difficult to modify existing algorithms
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9. Mathematica It’s not just for Calculus! Pro:
Tremendous amount of functionality with many available network and graph metrics.
Student familiarity with the Mathematica interface.
Con:
Expensive to maintain license upon departure from USMA (loss of developed tools).
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10. Gephi Get it here: https://gephi.org/ “Photoshop for graphs”
Active developer community (last release FEB 2016)
Very active online community (including Facebook users group)
Pro:
Premier graph visualization package
Entirely GUI
Intuitive to use
Shallow learning curve
Can visualize “Internet-sized” networks
Con:
Shorter list of analysis tools than other packages
For very large graphs, must get “under the hood” to adjust memory usage settings
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11. References
analysis-platforms
analysis-tools?smtNoRedir=1
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