The document summarizes a study comparing the social network structures of Facebook and LinkedIn groups for an organization in Milan called Milan In. Preliminary findings show the networks have different structures, with the LinkedIn group having a less dense and more centralized structure than the Facebook group. Future steps discussed are collecting more relationship and attribute data over time to analyze network evolution and compare other social networks.

1. Workshop on Advanced Research Methods
September 30, 2010
The structure of social network sites:
a comparison between Facebook and LinkedIn
– preliminary results –
Ivana Pais – University of Brescia (pais@jus.unibs.it)
Riccardo De Vita – University of Greenwich
Roberto Marmo – University of Pavia
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2. Agenda
 Social network sites: does one structure fit all?
 Empirical setting: the case of Milan In
 Methodology: exploring the data
 Preliminary findings: different levels of analysis
 Discussion and conclusion: the next steps
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3. Theoretical background
 ‘Appropriable social organisations’ vs intentional
organizations (Coleman, 1990)
 Technological development  online social capital
 Social networks are embedded in the variety of
communication artifacts available in the current
technoscape (Licoppe, Smoreda 2005)  different forms
of Social Network Sites : registration-based vs. connection
based, social vs. professional, online vs. offline…
(O’Murchu, Breslin, Decker, 2004; boyd, Ellison 2007)
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4. Research question
 The majority of the studies focused on the understanding
of the effects of social networks; while the factors that
generate, sustain and reproduce them partly remain to be
explored (Smith-Doerr & Powell, 2005).
 Little is known about the specific structure of online social
network services (Lewis et al. 2008)
RQ: Are different social network sites associated with
different network structures?
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5. Milan In
 A non-profit association set up in 2005 to allow members
of LinkedIn living in Milan to physically meet up with each
other.
 Comparative study:
o same organization & same actors
o Linkedin Group Vs Facebook Group
4311 505 1357
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6. Method
 Structural Variables: connection on Facebook and
Linkedin groups  symmetric networks
 Composition variable: gender, education, job title, number
of connections,...
 Exploratory analysis of several network properties at the
global and local level
 Software: UCINET 6 (Borgatti, Everett and Freeman,
2002) and helper applications
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7. Linkedin Group – Members since 2005

8. …adding the members since 2006

9. …adding the members since 2007

10. …adding the members since 2008

11. …adding the members since 2009

12. Linkedin Group – Today

13. Linkedin vs Facebook Group
Man; Woman
N Components Isolates Density Centralization Avg Degree
505 6 5 0.027** 83.4 % 13.5
505 35 33 0.019** 79.0 % 9.43
(+1 dyad)

14. Multiplexity
% of ties in the % of ties in the
Linkedin Group Facebook Group
2.00% 2.86%

15. Clique size
Overall Clustering Coefficient
Facebook Group 0.584
Linkedin Group 0.494

16. Identifying relevant actors
Degree Centrality Degree Centrality
Top 5 actors in Linkedin Group Top 5 actors in Facebook Group
ID Facebook Linkedin ID Facebook Linkedin
344 0 432 19 406 3
347 7 260 886 139 7
276 2 101 620 87 3
1031 5 91 696 75 2
16 25 90 497 74 40
5 Key Players
KPP 2 – Using nodes
Facebook Group Linkedin Group
714 344
19 16
394 321
482 1101
1236 530

17. Discussion
 Different network structures are associated with online
groups built for the same purpose but on different
platforms
o Specialization and different behaviour
 Implications for academic debate and for organization
management
 Need to take a process perspective in analyzing network
evolution
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18. The next steps…
 Data:
o Relations  recommendations, physical interaction
o Attribute data  questionnaire and deeper analysis
o Homophily
 Methodological approach:
o Longitudinal analysis
 Theoretical perspective:
o Network structure and organizational development
 Empirical setting:
o Comparison with other online social networks
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