Boston Data Swap 2013: Thinking with Networks
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Transcript of Boston Data Swap 2013: Thinking with Networks
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Katherine Ognyanova (Katya) Postdoctoral Research Fellow, The Lazer Lab
Email: [email protected] Website: www.kateto.net Twitter: @ognyanova
Boston Data Day
Skill-A-Thon 2013:
Thinking with networks
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Slide 2 www.kateto.net
Where do we see network data?
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Global Flights Network. Source: www.visualizing.org, 2012
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Internet Backbone: Each node represents an IP address. Source: Wikipedia
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Map of the Web: Nodes represent websites. Source: internet-map.net
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Network of Facebook friendships. Source: Facebook.com , 2010
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Wikipedia co-editing patterns among top editors in English. Source: Hogan, 2012
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Twitter network, @OIIOxford. Source: Hogan, 2011
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Adoption of topics in the US media system. Source: Ognyanova, 2013
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Global Spread of N1H1 and human mobility. Source: gleamviz.org, 2011
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Disease network: phenotype and disease gene associations. Source: Diseasome.eu, 2013
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Terrorism Network. Source: Business 2.0, December 2001. Six Degrees of Mohamed Atta
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Semantic Networks: Relationships between concepts. Source: Thinkmap Visual Thesaurus, 2012
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High-school Romance Network. Source: Easley & Kleinberg (2010) Networks, Crowds and Markets
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US Senate co-sponsorship network 1973-2004. Source: Fowler, 2006
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Network of news topics. Source: Slate News Dots, 2010
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Forrest Gump movie characters social network. Source: moviegalaxies.com, 2013
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Flavor network and the principles of food preparing. Source: Ahn, Ahnert, Bagrow & Barabasi , 2012
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Slide 20 www.kateto.net
What do we do with network data?
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Slide 21 www.kateto.net
Identification of key elements
Nodes CommunitiesLinks
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Slide 22 www.kateto.net
Variation in global network structure
Centralized Small WorldScale-free
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Slide 23 www.kateto.net
Drivers of link formation and dissolution
Reciprocity HomophilyTransitivity
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Slide 24 www.kateto.net
Dynamic networks and evolution over time
Time 1 Time 3Time 2
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Slide 25 www.kateto.net
Interdependence of structure and attributes / behavior
Selection: attributes or behavior affect structure
Influence: structure affects attributes or behavior
Time 1 Time 2
Time 1 Time 2
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Slide 26 www.kateto.net
Diffusion and contagion in networks
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Slide 27 www.kateto.net
Tools for network analysis & visualization
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Slide 28 www.kateto.net
Try these if you like writing scripts
R programming environment
User interface: RStudio
Network packages:
• Statnet (includes the packages network,
sna, ergm, latentnet, degreenet, networksis)
• igraph (available for R and Python)
• RSiena (modeling network evolution)
Python programming language
User interface: Anaconda, Spyder
Network packages:
• NetworkX (complex networks)
• igraph (available for R and Python)
R
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Slide 29 www.kateto.net
Try these if you like clicking on buttons
NodeXL is a Microsoft Excel add-on for
network visualization (Windows only).
It has importer plugins for network data
collection from Facebook, Twitter, and Flickr.
Ucinet is a classic software for network
analysis that has been around since the
1980s but is still regularly updated. It comes
with a basic visualization tool called NetDraw.
UCINET
Pajek is a software for
large-scale network
analysis and visualization.
Pajek
Gephi is an advanced tool
for graph visualization and
manipulation.
PNet is a suite of tools for
the simulation & estimation
of network models.
PNet
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My favorite network analysis platform:
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My favorite network visualization platform:
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Slide 32 www.kateto.net
What do network formats look like?
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• Link or no link (1 or 0)
Binary
• Positive or negative (+, - or 0)
Signed
• Weighted links (each link is assigned a value)
Valued
• Directed vs. undirected (undirected = symmetric)
Symmetric
• Multiplex (more than one link type)
Multiplex
A B
A B
+A B
5A B
A B
A few network types you will meet in the wild
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A B C D E
A 0 0 0 0 0
B 1 0 0 0 1
C 1 0 0 1 1
D 0 0 0 0 0
E 0 0 0 0 0
A
CB D
E
The matrix: directed networks
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A B C D E
A 0 1 1 0 0
B 1 0 0 0 1
C 1 0 0 1 1
D 0 0 1 0 0
E 0 1 1 0 0
A
CB D
E
A B C D E
A 0 1 1 0 0
B 1 0 0 0 1
C 1 0 0 1 1
D 0 0 1 0 0
E 0 1 1 0 0
The matrix reloaded: symmetric networks
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A B C D E
A 0 5 1 0 0
B 5 0 0 0 3
C 1 0 0 3 2
D 0 0 3 0 0
E 0 3 2 0 0
A
CB D
E
5
3
1
2
3
The matrix revolutions: valued networks
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B A 1
B E 1
C A 1
C E 1
C D 1
A
CB D
E
Source Destination Weight
Note: Weights are optional.
Save some bits: edgelist formats
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B A E
C A D E
A
CB D
E
Source Destinations
Save some bits: nodelist formats
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Frequently used network file formats
• GEXF (Graph Exchange XML Format)
• GraphML, another XML format for graphs
• GML (Graph Modelling Language)
• DL, edgelist & matrix formats used by UCINET
• Net and Paj, Pajek network and project files
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Slide 40 www.kateto.net
Coming up next:
Show-and-tell, dynamic network visualization.
Download this presentation & supporting files:
kateto.net/DataSwap