A Brief Introduction to
Social Network
Analysis
Jennifer Roberts
Outline
Description of Social Network Analysis –
Sociocentric vs. Egocentric networks
Estimating a social network
TRANSIMS – A case study
What is Social Network Analysis
(SNA)?
An analysis technique which studies
– Relationships between people and groups
– How those relationships arise
– Consequences of the relationships
(Christopher McCarty, Director of the UF Survey Research Center)
Nodes = people
Ties = relationships or interactions
Two Types of SNA
Egocentric Analysis
– Focuses on the individual
– Studies an individual’s personal
network and its affects on that individual
From redwing.human.net/~mreed/
warriorshtm/ego.htm
Sociocentric Analysis
– Focuses on large groups of people
– Quantifies relationships between people
in a group
– Studies patterns of interactions
and how these patterns affect
the group as a whole
From www.plant.uga.edu/dblmajor.htm
Egocentric SNA
Examines local network structure
Describes the network around a
single node (the ego)
– Number of other nodes (alters)
– Types of connections
Extracts network features
Uses these factors to predict health
and longevity, economic success,
levels of depression, access to new
opportunities
From www.stop.hu/
showcikk.php?scid=1005217
Sociocentric SNA
Quantifies relationships
and interactions between
a group of people
Studies how interactions,
patterns of interactions,
and network structure
affect
– Concentration of power
and resources
– Spread of disease
– Access to new ideas
– Group dynamics
Raines Laboratory Research Group, U. Wisconsin-Madison
http://www.biochem.wisc.edu/raines/people.html
Survey and Interview Data
Collection Techniques
– Name generator/name interpreter questions
Questions to elicit list of names: Who do you
discuss important matters with?
Follow-up questions: Reports about that persons
attributes, type of tie, ties between pairs of
contacts
– Inflow/outflow of resources
Expensive, subject to human error,
influenced by the nature of the questions
asked
Other Data Sources
Indirect measures
– Corporate records, event attendance, co-citations, coauthorship, trading patterns, shared affiliations, email,
phone calls, computer conferencing
– Non-invasive, inexpensive
– Not obvious how indirect measures relate to actual
interactions
Small scale methods
– Observation, diaries
Experimental
Accuracy
Surveys – accessing validity of people’s
reports
– Recall and observation do not match up -“people do not know, with any acceptable
accuracy, to whom they talk over any given
period of time”
– Evidence that people are good at recalling
typical interactions, bad at answering about
specific time scales
TRANSIM – A Method for
Estimating Large Social Networks
Assumes transportation network constrains
interactions
Creates a synthetic population
Models large-scale human interactions through
simulations
Used to study transportation planning, disease
propagation, mobile communications, and
demand within the electric
power grid
From www.environ.org.uk/Energy/Vehicle%20Fuels/
Creating a Synthetic Population
Data
– Land use and demographic census data
– Survey data about daily activities
Information used to create a synthetic
population with
– List of activities consistent with survey data
– Access to transportation consistent with
survey data
Activity Planning and Simulation
Creates schedule based on activity lists
– Optimizes sequential plan based on
transportation mode
– Updates schedule based on current
congestion levels
Simulation
– Bipartite graphs contains people nodes and
location nodes
– Simulation updates people’s locations every
second
Graph Structure
Set P of people nodes
Set L of location nodes
Edge (p, l) indicates p visits l on a normal day
L has a power law distribution with
= 2.8
– Number nodes with degree i equals
β
P’s distribution is concentrated around a small
average value
Theoretical Model
CL-Model
– Generates graphs based on expected degree
sequences
– Each node is assigned a weight, d(u), equal
to its expected degree
– Edge probabilities are proportional to node
weights and edge assignments are
independent
=
⋅
σ
σ=
=
Projection Graphs
Results
CL, fastgen approximation create
networks with similar properties to actual
data (fig 1)
Also include – approx algorithm
description, table, graphs of results,
projection graphs