2013 한국데이터사이언스 창립기념 심포지움 발표 - 삼성화재 이창헌

1. Chang Heon Lee
June 13, 2013
Studying Social Selection vs Social Influence
in Virtual Financial Communities

2.  Dynamics of Networks and Behavior
 Selection vs Influence
 Studying Selection vs Influence
 Stochastic Actor-oriented Model
 Advice Network
 Data
 Estimation Results
2
Outline

3. 3
Dynamics of Networks and Behavior
 Social network dynamics depends on individual behavioral
characteristics.
 Homophily vs. Heterophily
 But individuals’ behavior can depend on the network.
 Assimilation vs. Differentiation

4. 4
Selection vs Influence
 Selection
 Individuals make changes to their social ties as a result
of the behavior or characteristics of the ego, the alter,
and the dyad.
 Influence
 Individuals’ behavior changes as a function of
interaction with alters.

5. 5
Studying Selection vs Influence
How can we separate cause and effect?
Net(tn)
Structural Effects
Behavioral Effects
Net(tn+1)
Beh(tn) Beh(tn+1 )

6. 6
Stochastic Actor-oriented Model
 Assumption of stochastic actor-oriented models
 Network actors drive the process: individual decisions;
• decisions about network selection or termination
• decisions about own behavior
 Longitudinal versions of Exponential Random Graph Model
(ERGM)
 Assumption: network change driven by change in tie
variables

7. 7
Stochastic Actor-oriented Model-cont.
 ( , )i k ikk
f x s x   
 Network micro step
 individual decisions options
- change tie variable to one other actor
- or change nothing
 maximize an objective function with respect to the network
configuration.
 The probability that actor i changes his ties variable with j is
1
exp( ( , ( 젨? )
( , )
exp( ( , ( 젨? )






i
ij n
i
k
f x i j
p x
f x i j
↝
↝

8. 8
Stochastic Actor-oriented Model-cont.
 Model Parameters
 Estimated from observed data
 Stochastic simulation models
•Markov Chain Monte Carlo(MCMC) algorithm
•Approximate the solution of the Method of Moment
 Parameter Estimation
 Choose statistics
 Obtain parameters such that the expected values of the statistics
are equal to the observed values
 Expected values are approximated as the averages over a lot of
simulated network
 Observed values are calculated from the dataset (target values)↝

9. 9
 Organizational scholars have framed the advice network in
terms of information transmission, knowledge transfer,
and joint problem solving.
 Simply, stock message board sites include;
Advice Network in Virtual Financial Community
I
K
J
I
K
J

10. 10
Data
 Snowball Sample from the largest Australian VFCs
 The network consists of 707 active users.
 Panel Data
 The network data is divided into three successive two-month
periods.

11. 11
Estimation Results
[Representation of Selection and Influence Effects]
Baseline Network Structure
Contribution Behavior
Changes in Individual
Contribution Behavior
Changes in Peer Network
Time 1 Time 2
Structural Effects
Behavioral Tendencies

12. Results(1): Structural Effects
Effects
Parameter
Estimate
Standard Error p-value
Endogenous Effects
Out-degree -2.798 0.287 <.001***
Reciprocity 2.753 0.135 <.001***
Popularity-Alter 0.487 0.073 <.001***
Activity-Alter -0.084 0.022 <.001***
In-In Degree Assortativity 0.057 0.015 <.001***
Out -Out Degree Assortativity -0.035 0.017 <0.05*
In-Out Degree Assortativity 0.085 0.02 0.42
Out-In Degree Assortativity 0.005 0.011 <.001***
1212
*p <0.05; **p <0.01; ***p <0.001

13. 1313
Results(2): Selection
Effects
Parameter
Estimate
Standard
Error
p-value
Contribution Quantity Effects
Contribution Quantity-Alter 0.328 0.127 <.001**
Contribution Quantity -Ego -0.352 0.201 0.080
Contribution Quantity -Similarity 0.443 0.467 0.949
*p <0.05; **p <0.01; ***p <0.001

14. 1414
Results(2): Selection
 Contribution behavior as antecedent to advice network structure
 Individuals who are salient in terms of contribution quantity
are more likely to be sought as an advice partner for
repeated advice exchanges over time.
 People don’t select similar others when seeking information.

15. 1515
Results(3): Influence
Effects
Parameter
Estimate
Standard
Error
p-value
Rate Function
Rate of Contribution Quantity Change 1 192.130 17.352 <.001***
Rate of Contribution Quantity Change 2 158.051 14.952 <.001***
Rate of Contribution Quantity Change 3 175.543 15.093 <.001***
Linear Shape 0.1312 0.2834 0.463
Quadratic Shape -0.0103 0.0155 0.665
Contribution Quantity- In-degree 0.0096 0.0028 <.001***
Contribution Quantity- Out-degree -0.0094 0.0124 0.758
Contribution Quantity Total Similarity 0.4754 0.2210 <0.05*
*p <0.05; **p <0.01; ***p <0.001

16. 1616
Results(3): Influence
 Contribution behavior as outcome of advice network structure
 The greater the number of incoming advice ties to an
individual, the higher the quantity of contributions he makes
to the community over time. Thus, individuals adjust their
level of contribution quantity as a result of their advice tie
formation.
 But, individuals contribution in terms of the number of
postings are likely to become similar to that of other
partners.

17. 1717
Conclusions
 Influence rather than Selection
 Individual adjust their level of contribution to that
their peers (patterns of assimilation).
 There is no patterns of homophily.