Using Social Network Analys

Using Social Network Analys

Im

plement Innovations in Heal

November 15 2012 November 15, 2012

VA Cyberseminar Series 

on Enhancing Implementation Sciencon Enhancing Implementation Scienc

Thomas W. Valente, PhD Thomas W. Valente, PhD

Professor, Institute for Prevention Rese

Preventive Medicine, Keck School of Me

University of Southern California

tvalente@usc.edu
–

Acknowledgements

• National Institutes of Health

– NIAA RC1AA019239– NCI: R0110866440
Outline

1) Diffusion and behavior change

2) Network models of diffusion

3) Social influence

4) N t e work i t n erventions
Some ve read some research on

–

Poll Question:

• How much experience do you have wit

network analysis?

– Minimal (don’t know what it is)– Some (have read some research on it)– Taken a class or workshop

Used it a little (have used or seen networ

networ

research in my work)

– A lot (have conducted conducted SNA)
How Does Change Happe

Happe

New ideas and products enter a system 

some external source (person, media, te

change) change) and then spread through through interp

interp

contact.

Hypothetical Diffusion When Adopters Persuade Non‐

Rate of One Percent Percent (Homogenous (Homogenous or Random M

M

Time Cumulative Non-adopters Rate 

A

1 0.00 100.00 0.01 

2 5.00 95.00 0.01 3 9.75 90.25 0.01 4 18.55 81.45 0.01 

5 33 66. 66 34. 0 01 . 6 55.99 44.01 0.01 7 80.63 19.37 0.01 8 96.25 3.75 0.01 

9 99.86 0.14 0.01 

10 100 00. 0 00 . 0 01 . 
Diffusion Diffusion for Random M

100 

60 80 

pters 

40 

rcent Adop 

20 

Per

0 

1 2 3 4 5 6 7 8 9 10 

Time 
0 00

Diffusion of Tetracycline for Int

versus Marginal Marginal Doctors Doctors (CKM

1.00 0.80 0.60 

dopters 

0.40 

Percent Ad 

0.20 

P 

0.00 

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 1

Time 
Opinion leadership and contagio

product product diffusion diffusion. Marketing Marketing Sc

Sc

• Iyengar, R.,Van den Bulte, C. & Valen

(2011). 

• Data on discussion and referral ties 

cities: SF, LA & NY 

• Prescribingdata from product launc

years 
Discussion Network for S
Network Effect on Product Ado

Adopted 1Q Post Launch Adopted 2Q Post Launch Adop Q ted 3Post Launch Adopted 4Q Post Launch Adopted 5Q Post Launch Adopted 6Q Post Launch Adopted 7Q Post Launch Adopted 7Q Post Launch Adopted 8Q Post Launch 

Iyengar, R., Van den Bulte, C., Valente, T. (in press). 

Opinion Leadership and Social Contagion in New Product Diffusion Opinion Leadership and Social Contagion in New Product Diffusion. 

Marketing Science. 
Sociometric Sociometric v. Self Report

Report

• Correlated at approximately 0.43 (p<0• Self reported OLs less susceptible to p

influence

• Sociometric Sociometric OLs no more or less susce

susce
Dif

Coun

fusion at Policy Level

try Ratifications of FCTC

100 

r80 cent Italy 

60 80 

p tion Per Lebanon 

40

ve Adop

Iceland 

20 

Cumulati Norway 

0 

2003 2003 2004 2004 2004 2005 2005 2005 2006 2006 2006 2007 2007 2007 2

C 

200 200 200 200 200 200 200 200 200 200 200 200 200 200 2

Time 
Co-membershipon Glob

Earliest (15.5%) Non-Ratifier

Nauru San Marino Seychelles Qatar

Palau Andorra 

Bahamas Afghanistan Cuba Czech Republic Eritrea

Ethiopia Mexico Gabon 

Mal Haiti U.S.A. ta Lib i era Cote d'Ivoire Malawi 

Mauritius Monaco 

India Fiji Mozambique Switzerland Slovakia Republic of Moldova Brunei 

Saint Kitts and Nevis Saint Vincent and the Grenadines Japan Hungary Sierra Leone Sierra Leone A

Somalia Morocco 

Mongolia Suriname Tajikstan Norway Turkmenistan Indonesia Tunisia

New Zealand

Dominican 

BosniaKenya S i L Sri anka Myanmar

Iceland

Singapore 

Bangladesh 

Cook Islands Maldives 
3 Treaties Treaties Diffused Diffused 2001‐20

FCTC Corrupt Pollute 

1 

0 9. 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 

0 
Social Influence

• Primarily from interpersonal networks
Network Exposur Exposur

= Non User = Non User = User 

Network Network N

Exposure=20% Exposure=40% Expo
Network Exposure: Indirec

= Non User = User 

PN Exposure=54.7%
Network Exposure: Structural Equi

A B
Network Exposure: Tie St

Network Exposure: Tie St

Influence is 

Stronger Stronger 

for Stronger 

(Closer )Ties 

5 

4

1

1
Network Exposure: Const

1 2 

4 5
Network Thresholds

PN Exposure=33% PN Exposure=33% PN Thresh

Low Threshold Adopter Low Threshold Adopter High Thres

High Thres

= Non-FP User User -FP = FP User User 

PN Threshold=33% PN Threshold=33% 
13283

12 25 

Graph of Time of Adoption by Network Th

One Korean Family Planning Planning Commu

Commu

Threshold
 

11 37 

15 

20 57 

52 60 

14 

22 

5 

59 

7 

35 

36 

8 

53 16 31 64

100% 

0% 

1963 Time 
Network Network Influence Influence Weigh

Weigh

1. Direct influence

2. Indirect ties

3. Structural equi l va ent ties4. Tie strength (e.g., best friends)5. Simmelian ties6. Density weighted7. Degree weighted weighted (or other centrality centrality measures) measures)8. Attribute weighted (boy friends)9. Selection effects10. Thresholds
Dynamic Dynamic Estimation Estimation of Diffusion Diffusion 

Influence Selection Leaders External Influence

ect Size Eff

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 

Time
Dynamic Estimation

• External influence decreases as the nu

available network influences increase.

• Network exposure influence increases 

prevalence.

• Selection effects decrease

• Opinion leader influences vary being m

in the beginning peaking at mid‐point 

decreasing
An alternative:

• Construct network interventions

– Manipulate networks– Test their influence under laboratory and 

world conditions;

– Improve organizational performance and 

health outcomes.
Network Network Interventions: Interventions: Acceleratin

Acceleratin

Defining:• Using network data to change behaviors• Change individual individual and community/organization

community/organization

• Network interventions are any change program 

uses network data to:

– Select change agents

– Define groups

– Affect network structure

– Assist behavior change program implement
Principle Principle 1: Program Program Goals M

• In some cases want to increase cohesi

others increase fragmentation

• Increase/decrease centralization

• E g. ., slowing slowing spread of STDs requires requires d

d

strategy than accelerating adoption of 

automation

• Network Interventions Are not Agnost

Content.
Principle Principle 2: Theory

• The type of change desired will be 

by theory (e.g., changing behaviors attitudes).

• Understanding Understanding motivations motivations for and

barriers against behavior change is 

• A well‐articulated articulated theory of the be

be

often critical for successful interve
Principle Principle 3: Learn As Well As Ind

• The interventionist interventionist should use netw

netw

methodology to learn from the com

as much as try to i fl n uence it.

• Programs which meet the needs of 

audiences are better received than 

designed asymmetrically.
Network Interventions, Val

Science, 2012

Strategy Tactic Operatio

Identification Leaders

BridgesKey PlayersPeripheralsLow Thresholds

Degree, Closeness Closeness, 

Mediators, Bridges

Positive, Negative

Proportions, Count

Segmentation Groups Positions Components, Cliqu

Structural Equivalen

Induction WOM

SnowballMatching

Random ExcitationRDS, OutreachLeaders 1st, Groups 

Alteration

(Manipulation)

Deleting/Adding NodesDeleting/Adding LinksRewiring

VitalityOn Cohesion, OtheOn Network, On Be
1. Identify Key Change Agents 

Network Data

• Many different network positions ca

identified : 

A. Leaders B. Key Players Key PlayersC. BridgesD. Marginals – Peripherals – Isolates Isolates E. Low threshold adopters 
1. Opinion Opinion Leaders Leaders

• The most typical network intervention

• Easy to measure

• Intuitively appealing• Proven effectiveness

• Over 20 studies using network data to 

OLs and hundreds of others using othe

identification techniques
Diffusion Network Simulation w/ 3 

Adopter Conditions Adopter Conditions

80 

100 

ers 

40 60

Adopte 

20 40 

Percent 

0 

1 2 3 4 5 6 7 8 9 1 0 

P 

Time 
Work with Opinion Opinion Opinion Lead

Lead

• Identify them• Recruit them Recruit them• Convert them (if need be) • Use them • Valente & Pumpuang (2007) 

identified 20 sociometric studie

OLs for behavior change 
Network Network Network Positions ositionsositions

P

Bridges 

Central Members 

Isolate 
Network Interventions

Strategy Tactic Operatio

Identification Leaders

BridgesKey PlayersPeripheralsLow Thresholds

Degree, Closeness Closeness, 

Mediators, Bridges

Positive, Negative

Proportions, Count

Segmentation Groups Positions Components, Cliqu

Structural Equivalen

Induction WOM

SnowballMatching

Random ExcitationRDS, OutreachLeaders 1st, Groups 

Alteration

(Manipulation)

Deleting/Adding NodesDeleting/Adding LinksRewiring

VitalityOn Cohesion, OtheOn Network, On Be
2. Segmentation

• Intervention is directed toward or inclu

whole group of people

• Segmentation interventions identify an

expect a whole group to adopt the inn

inn

at the same time
Groups

• Sets of people/nodes that are densely

connected

• Groups can reinforce (or inhibit) the be

change process

• Behavior change may be appropriate f

groups

• There are many network methods use

groups
15 

20 

3 

32 

2 

20 

30 

33 

37 

19 

21 

35 

8 

1 

17 

31 

4 

12 

18 

27 

29 

7 

12 

23 

7 

26 5 

11 

13

6 

34 

9 
3. Induction: Matching Lea

Groups

• Rather than have leaders leaders unattach

unattach

assign them to people who think t

leaders

• Leadership is local• Emphasizes homophily b t e ween l 

e

and members

• Builds on naturally occurring netw

• Leaders can be more effective if as

to those who nominate them
Network Influence is Proxima
2 

7 

Matching Leaders to Gr Matching Leaders to Gro

Sociogram based on ties 

33 32 4

10 28

13

8 

15 

30 14

1

6

3

29

5

9

11

31

1(a) 

Optimal leader/l

8 

13 

15 

12 
Hypothetical Network for Graphical 

Hypothetical Network for Graphical 
duct o

Network Interventions Network Interventions

Strategy Strategy Tactic Operatio

Operatio

Id tifi ti Identification Leaders Leaders 

Bridges Key Players Perip heralsLow Thresholds 

Degree ClosenesDegree, Closenes

Mediators, BridgePositive, Negative Proportions, Coun

Segmentation Groups Positions Positions Components, Cliq

Structural EquivalStructural Equival

Induction WOM 

Snowball Matching 

Random Excitatio

RDS, Outreach Leaders 1st, Grou

Alt tiera on 

(Manipulation) 

Deleting/Adding Nodes Deleting/Adding Nodes Deleting/Adding Links Rewiring 

Vitality On Cohesion, OthOn Network, On B
Degree Clos

Clos

Between Keyplay
Keyplayers 

Bridges Thresh
Groups Positio

Hierarchies 
WOM Sno

Outreach M
Vitality No

Link Deletion Link Addi
Rewire SW Rewire Behav
Graphical Displays of Intervention 
Selecting a NI Selecting a NI

• Availability and type of data 

– 

T

ypes of networks 

– Existing network structure 

• Behavioral characteristics Behavioral characteristics 

– Existing prevalence – 

Perceive

d c

harac

teristi cs suc

h as cult 

compatibility; cost; trialability; etc. 
Linking Theory to Intervention S

Linking Theory to Intervention S

• There are several theoretical mech

that drive contagion and behavior c

• Evidence for a particular mechanism

suggests choice of intervention strasuggests choice of intervention stra

tactic 
t

Influence Mechanisms Aligned with 

Choices

Mechanism Tact

Power 

Conflict Cohesion Cohesion Isolation Thresholds 

Leaders Bridges 

Key Players Key Players Peripherals Low Thresholds 

Group Identification Structural Equivalence 

Groups Positions 

Information diffusion 

Hard to reach populations Closure Homophily 

WOM Snowball Outreach Matching 

Att ibr utes 

Structure 

Structure!! 

D l ti /Addi Deleting/Adding 

Deleting/Adding 

Rewiring 
Leadership & Influence have 2 Dim

T 

ru

Low 

Expertis 

e 

Low 

High 
Discussion + Advice Nominations in th

Atlantic (Osteo.)
Prominence Nominations in the Sout

(Osteo.)
Poll Question:

• Would you like to do a network interve

– No, not applicable to my work– Yes, but no time soon– Yes, but I don’t know how– Yes, help me!
ConclusionsConclusions Conclusions

• Network theories and methods can be 

for behavior change interventions. 

• Network interventions have the prom

improving outcomes (health, organizatimproving outcomes (health, organizatperformance, etc.); and 

• T t th ti ll i t ti t k 

Test theoretically interesting network 
Prospects

• SNA is a “hot” topic now and many pe

organizations, communities, and other interested in using the method. 

• Funders are now willing to pay for SNA Funders are now willing to pay for SNA 

research and application. 

• Experiments prov

i

ide an opportunity tt o 

how networks work. 
More reading and informa

www

-hsc usc edu/~tvalen

hsc

.usc

.edu/~tvalen
Questions??

• About SNA?

• About network interventions?

• Additional resources?