Social Network Analysis

(1)

A Brief Introduction to

Social Network

Analysis

Jennifer Roberts

(2)

Outline

Description of Social Network Analysis

–

Sociocentric

vs. Egocentric networks

Estimating a social network

TRANSIMS

(3)

What is Social Network Analysis

(SNA)?

An analysis technique which studies

–

– Relationships between people and groupsRelationships between people and groups –

– How those relationships ariseHow those relationships arise –

– Consequences of the relationshipsConsequences of the relationships

(Christopher McCarty, Director of the UF Survey Research Center) (Christopher McCarty, Director of the UF Survey Research Center)

Nodes = people Nodes = people

Ties = relationships or interactions Ties = relationships or interactions

(4)

Two Types of SNA

Egocentric Analysis Egocentric Analysis

–

– Focuses on the individualFocuses on the individual –

– Studies an individualStudies an individual’’s personals personal

network and its affects on that individual

Sociocentric

Sociocentric AnalysisAnalysis

–

– Focuses on large groups of peopleFocuses on large groups of people –

– Quantifies relationships between people Quantifies relationships between people in a group

in a group

–

– Studies patterns of interactionsStudies patterns of interactions and how these patterns affect

and how these patterns affect

the group as a whole

From redwing.human.net/~mreed/ warriorshtm/ego.htm

(5)

Egocentric SNA

Examines local network structure Examines local network structure Describes the network around a Describes the network around a

single node (the ego) single node (the ego)

–

– Number of other nodes (alters)Number of other nodes (alters) –

– Types of connectionsTypes of connections

Extracts network features Extracts network features

Uses these factors to predict health Uses these factors to predict health

and longevity, economic success, and longevity, economic success,

levels of depression, access to new levels of depression, access to new

opportunities opportunities

From www.stop.hu/ showcikk.php?scid=1005217

(6)

Sociocentric

SNA

Quantifies relationships Quantifies relationships

and interactions between and interactions between

a group of people a group of people

Studies how interactions, Studies how interactions,

patterns of interactions, patterns of interactions,

and network structure and network structure

affect affect

–

– Concentration of power Concentration of power and resources

and resources

–

– Spread of diseaseSpread of disease –

– Access to new ideasAccess to new ideas –

– Group dynamicsGroup dynamics _{Raines Laboratory Research Group, U. Wisconsin-Madison}

(7)

Survey and Interview Data

Collection Techniques

–

– Name generator/name interpreter questionsName generator/name interpreter questions

Questions to elicit list of names: Who do you

discuss important matters with?

Follow

Follow--up questions: Reports about that persons up questions: Reports about that persons attributes, type of tie, ties between pairs of

attributes, type of tie, ties between pairs of

contacts

–

– Inflow/outflow of resourcesInflow/outflow of resources

Expensive, subject to human error,

influenced by the nature of the questions

asked

(8)

Other Data Sources

Indirect measures Indirect measures

–

– Corporate records, event attendance, coCorporate records, event attendance, co--citations, cocitations, co- -authorship, trading patterns, shared affiliations, email,

authorship, trading patterns, shared affiliations, email,

phone calls, computer conferencing

–

– NonNon--invasive, inexpensiveinvasive, inexpensive –

– Not obvious how indirect measures relate to actual Not obvious how indirect measures relate to actual interactions

interactions

Small scale methods Small scale methods

–

– Observation, diariesObservation, diaries

Experimental Experimental

(9)

Accuracy

Surveys

–

accessing validity of people

’

s

reports

–

– Recall and observation do not match up Recall and observation do not match up -- --“

“people do not know, with any acceptable people do not know, with any acceptable accuracy, to whom they talk over any given accuracy, to whom they talk over any given period of time

period of time”” –

– Evidence that people are good at recalling Evidence that people are good at recalling typical interactions, bad at answering about typical interactions, bad at answering about specific time scales

(10)

TRANSIM

–

A Method for

Estimating Large Social Networks

Assumes transportation network constrains Assumes transportation network constrains

interactions interactions

Creates a synthetic population Creates a synthetic population

Models large

Models large--scale human interactions through scale human interactions through simulations

simulations

Used to study transportation planning, disease Used to study transportation planning, disease

propagation, mobile communications,

propagation, mobile communications, and and demand within the electric

demand within the electric power grid

power grid

(11)

Creating a Synthetic Population

Data

–

– Land use and demographic census dataLand use and demographic census data –

– Survey data about daily activitiesSurvey data about daily activities

Information used to create a synthetic

population with

–

– List of activities consistent with survey dataList of activities consistent with survey data –

– Access to transportation consistent with Access to transportation consistent with survey data

(12)

Activity Planning and Simulation

Creates schedule based on activity lists

–

– Optimizes sequential plan based on Optimizes sequential plan based on transportation mode

transportation mode –

– Updates schedule based on current Updates schedule based on current congestion levels

congestion levels

Simulation

–

– Bipartite graphs contains people nodes and Bipartite graphs contains people nodes and location nodes

location nodes –

– Simulation updates people’Simulation updates people’s locations every s locations every second

(13)

Graph Structure

Set

Set PP of people nodesof people nodes Set

Set LL of location nodesof location nodes Edge

Edge (p, l)(p, l) indicates indicates pp visits visits ll on a normal dayon a normal day

L

L has a power law distribution with has a power law distribution with = 2.8= 2.8

–

– Number nodes with degree Number nodes with degree ii equalsequals

P

P’’ss distribution is concentrated around a small distribution is concentrated around a small average value

average value

(14)

Theoretical Model

CL

-

Model

–

– Generates graphs based on expected degree Generates graphs based on expected degree sequences

sequences –

– Each node is assigned a weight, Each node is assigned a weight, d(ud(u)), equal , equal to its expected degree

to its expected degree –

– Edge probabilities are proportional to node Edge probabilities are proportional to node weights and edge assignments are

weights and edge assignments are independent independent

=

⋅

=

σ

(15)

Projection

(16)

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