This document discusses strategies for dealing with large social network data, including cleaning redundant data, filtering vertices or edges, and analyzing one-mode and two-mode networks. It also introduces ego-centric networks and varying the degree of separation to analyze an ego's extended network.

1. Chapter 6
PREPARING DATA AND
FILTERING, TWO-MODES
NETWORK, EGO-CENTRIC
NETWORK
 Presentation based on Hansen, D., Shneiderman, B., & Smith, M. A. (2011).
Analyzing Social Media Networks with NodeXl: Insights from a Connected World.
New York, NY: Morgan Kaufmann
 Please provide acknowledgement for use as follows:
Kwon, H. (2013). “Social Network Analysis :Basics.” Lecture Presentation.
Arizona State University.

2.  Large data
(1) too many redundant edges
(2) too many vertices
 Problem?
Hard to analyze/interpret
Hard to visualize
DEALING WITH LARGE DATA

3.  Two strategies how to deal with large data
1) Clean up relational data by merging duplicate
edges BEFORE metrics analysis and visualization:
Transforms a un-weighted (binary) network into a
weighted network
2) Filter data by REMOVING selected vertices or
edges: For example, only consider the top 50 most
active vertices. As another example, identify subsets
of a network and compare them.
MAKE THE LARGE DATA EFFICIENT!

4. One-Mode Network
1. “Typical” network
2. All vertices are the
same kind.
3. Les Miserable
network
4. Vertex column 1 and
2 includes same
names
Two-Modes Network
1. “Affiliation” network
2. Two different set of
vertices
3. Example: Serious Eat
network
4. Vertex column 1 and
2 includes completely
different names
TWO TYPES OF NETWORK

5. EGO-CENTRIC NETWORK: 1.5 DEGREE OF
SEPARATION (DEFAULT)
Whole Ego

6. EGO-CENTRIC NETWORK: 2 DEGREE OF
SEPARATION
Whole Ego

7. EGO-CENTRIC NETWORK: 2.5 DEGREE OF
SEPARATION
Whole Ego