The document discusses network graphs and provides examples of how they can be used. A network graph models connections between nodes and edges that can represent relationships between people, organizations, or other connected entities. Network graphs allow visualization of patterns in complex data and analysis of network properties like central nodes, connection strengths, and community structure. The document then provides a sample analysis using a congressional voting records dataset to construct a network graph and analyze which bills shared common supporters and differences in voting patterns between political parties.

1. UP AND RUNNING WITH
NETWORK GRAPHS
Connections
Sarah Cullem

2. Inspiration

3. Node Edge
Systems of nodes and
edges that form a
structured representation
of connected things and
how they are related
What is a network graph?

4. Node Edge
Edges can have weight that
describes the strength of a
relationship
What is a network graph?

5. Node Edge
Edges can be lines
(undirected graphs) or
arrows (directed graphs)
What is a network graph?

6. Node Edge
Nodes can vary in size
based on the degree of
their relationships to other
nodes
What is a network graph?

7. Node Edge
Nodes can vary in color
based on selected
attributes
What is a network graph?

8. Network graphs allow you to visualize
patterns in complex, multidimensional data
Who/what is the most central in the network?
How strong are the connections between nodes?
What is the shortest path between nodes?
Are there communities within the larger network?
How dense is the network?
Who/what is the most influential?

9. Micro vs. Macro
Level
Static vs.Time
Series
Social Semantic Geographic
One vs. Many
Dimensions
Types of Relationships
Analytical Decisions

10. Graph Type: Directed
Nodes: Girls in a 1960s dormitory
Edges: 1st & 2nd choices when asked which other girls
they liked best as their dining partners
http://www.slideshare.net/ValaAliRohani/social-network-analysis-part-1-40046689

11. Graph Type: Undirected
Nodes: Adverse drug reactions in instagram posts
Edges: Co-occurrence of adverse reactions to drugs
treating depression
http://psb.stanford.edu/psb-online/proceedings/psb16/brattigcorreia.pdf

12. http://truthy.indiana.edu/highlights/
Social Butterflies or Frequent Fliers?
Twitter trends follow similar patterns as epidemics, diffusing across the
country along the same pathways as human travelers

13. And now for an example using the
CongressionalVoting Records dataset
https://archive.ics.uci.edu/ml/datasets/Congressional+Voting+Records

14. Type: Undirected Graph
Nodes: Names of bills and party affiliation
Edges: Every pairing of “yes” on bills or
party affiliation
Weight: # of times each bill-bill or bill- party
pairing appears as “yes” together
Example 1:Which bills share common
voters/parties?

15. Education Religious Republican Democrat
Voter A Yes Yes Yes No
Voter B NotYes Yes No Yes
Voter C Yes NotYes Yes No
Voter D NotYes NotYes No Yes
Our initial dataset consists of each voter, their
vote on each bill, and their party affiliation
Note: Using “NotYes” for bills instead of “No” because data set contains cases of “?” where the vote was neither “yes” nor “no”

16. Education Religious Republican Democrat
Voter A 1 1 1 0
Voter B 0 1 0 1
Voter C 1 0 1 0
Voter D 0 0 0 1
We recode all “Yes” votes to 1 and “No/Not
Yes” votes to 0
Note: Using “NotYes” for bills instead of “No” because data set contains cases of “?” where the vote was neither “yes” nor “no”

17. Education Religious Republican Democrat
Voter A 1 1 1 0
Voter B 0 1 0 1
Voter C 1 0 1 0
Voter D 0 0 0 1
Nodes
Education
Religious
Republican
Democrat
Our nodes are the bill and party
“tags” for each voter

18. Edges Weights
Education, Religious
Education, Republican
Education, Democrat
Religious, Republican
Religious, Democrat
Republican, Democrat
Our edges are all possible
“bill-bill” and “bill-party”
combinations in the dataset
Education Religious Republican Democrat
Voter A 1 1 1 0
Voter B 0 1 0 1
Voter C 1 0 1 0
Voter D 0 0 0 1

19. Edges Weights
Education, Religious
Education, Republican
Education, Democrat
Religious, Republican
Religious, Democrat
Republican, Democrat
Our weights are the
number of times our
nodes/tags occur together
Education Religious Republican Democrat
Voter A 1 1 1 0
Voter B 0 1 0 1
Voter C 1 0 1 0
Voter D 0 0 0 1

20. Education Religious Republican Democrat
Voter A 1 1 1 0
Voter B 0 1 0 1
Voter C 1 0 1 0
Voter D 0 0 0 1
Edges Weights
Education, Religious 1
Education, Republican 2
Education, Democrat 0
Religious, Republican 1
Religious, Democrat 1
Republican, Democrat 0
There is one case where a
voter voted “yes” on
Education and on Religious

21. Education Religious Republican Democrat
Voter A 1 1 1 0
Voter B 0 1 0 1
Voter C 1 0 1 0
Voter D 0 0 0 1
There are two cases where a
voter was Republican and also
voted “yes” on Education
Edges Weights
Education, Religious 1
Education, Republican 2
Education, Democrat 0
Religious, Republican 1
Religious, Democrat 1
Republican, Democrat 0

22. Which bills share common voters/parties?
Network graph created with open source tool Gephi: https://gephi.org/

23. On average, 70% of
democrats and 20%
of republicans
voted yes
On average, 30% of
democrats and 79%
of republicans
voted yes
Network graph created with open source tool Gephi: https://gephi.org/

24. Network graph created with open source tool Gephi: https://gephi.org/
56% of democrats
and 56% of
republicans voted
yes
20% of democrats
and 89% of
republicans voted
yes
72% of democrats
and 14% of
republicans voted
yes

25. Do members of both parties vote together?
Network graph created with open source tool Gephi: https://gephi.org/

26. • My GitHub repo: https://github.com/scullem/network_graphs
• NetworkX python: https://networkx.github.io/
• NetworkX tutorial: https://github.com/sarguido/networkx-tutorial
• Gephi download: https://gephi.org/
• Sebastien Heymann Exploratory Network Analysis with Gephi
Part 1: https://www.youtube.com/watch?v=Y7Ah6VylIak
• Gephi guides: http://meta-guide.com/videography/100-best-gephi-
videos
• Graph Analysis andVisualization: Discovering Business
Opportunity in Linked Data: http://www.amazon.com/Graph-
Analysis-Visualization-Discovering-Opportunity-ebook/dp/
B00T1JQUQE
Resources