In this paper, we analyze the influence of social status on opinion dynamics and consensus building in collaboration networks. To that end, we simulate the diffusion of opinions in empirical collaboration networks by taking into account both the network structure and the individual differences of people reflected through their social status. For our simulations, we adapt a well-known Naming Game model and extend it with the Probabilistic Meeting Rule to account for the social status of individuals participating in a meeting. This mechanism is sufficiently flexible and allows us to model various situations in collaboration networks, such as the emergence or disappearance of social classes. In this work, we concentrate on studying three well-known forms of class society: egalitarian, ranked and stratified. In particular, we are interested in the way these society forms facilitate opinion diffusion. Our experimental findings reveal that (i) opinion dynamics in collaboration networks is indeed affected by the individuals’ social status and (ii) this effect is intricate and non-obvious. In particular, although the social status favors consensus building, relying on it too strongly can slow down the opinion diffusion, indicating that there is a specific setting for each collaboration network in which social status optimally benefits the consensus building process. Paper: http://www.know-center.tugraz.at/cms/wp-content/uploads/2015/08/ASONAM_2015_Paper.pdf Reference: Hasani-Mavriqi I, Geigl F, Pujari SC, Lex E, Helic D (2015) The influence of social status on consensus building in collaboration networks. In: Proceedings of the 2015 IEEE/ACM international conference on advances in social networks analysis and mining 2015, ASONAM ’15ACM, New York, NY, USA, pp 162–169 http://dl.acm.org/citation.cfm?id=2808887&CFID=851242713&CFTOKEN=32991930

1. http://Learning-Layers-euhttp://Learning-Layers-eu
Learning Layers
Scaling up Technologies for Informal Learning in SME Clusters
The Influence of Social Status on
Consensus Building in Collaboration
Networks
Ilire Hasani-Mavriqi, Florian Geigl, Subhash Chandra Pujari, Elisabeth Lex, Denis Helic
1
Austrian Science Fund: P 24866-N15

2. http://Learning-Layers-eu
• We tend to create connections
and interact with people who
have a high social status in our
community
• Our behaviour, our opinions are
often influenced by actions of
such people
• Example: university class – a
mentor influences opinions of
her student during consensus
building
Social Status
2

3. http://Learning-Layers-eu
Social Status and Consensus
Building
• Influence of social status on opinion dynamics is
moving from offline to online
• Focus:
– Investigate the influence of social status on dynamical
processes that take place in collaboration networks
– Study the interplay between structure, dynamics and
exogenous node characteristics and how these
complex interactions influence the process of
consensus building
3

4. http://Learning-Layers-eu
Contributions
• Methodologically
– Naming Game (statistical physics) is extended with the
Probabilistic Meeting Rule
– Individual differences between nodes in the network are
considered
– Through parametrization, explore the emergence and
disappearance of social classes in collaboration networks
• Empirically
– Simulate peer interactions in empirical datasets
(StackExchange Q&A sites), assuming that the status
theory holds and observe the consequences
4

5. http://Learning-Layers-eu 5
Naming Game Meeting

6. http://Learning-Layers-eu 6

7. http://Learning-Layers-eu
Probabilistic Meeting Rule Equation
psl = min (1, e β(ss – sl))
ss – speaker‘s status
sl – listener‘s status
β ≥ 0 – stratification factor (tuning parameter)
7

8. http://Learning-Layers-eu
The emergence of social classes
based on the stratification factor β
8
β = 0, psl is always 1 -> egalitarian society
β = 0.0001, psl decays [0,1] –> ranked society
β = 1, psl is 0 –> stratified society

9. http://Learning-Layers-eu
Datasets
9
• Datasets from Q&A site StackExchange
• Reputation scores – proxy for social status
• 6 language datasets
#nodes (n), #edges(m), mean (µ), median (µ1/2), standard
deviation (σ) of the reputation scores, assortativity coefficient (r)
and modularity (Q)

10. http://Learning-Layers-eu
Datasets – distribution of
reputation scores
10

11. http://Learning-Layers-eu
Simulations
• The simulation framework is provided as an open source project [1]
• 2 m interactions for the English network, 1 m for other networks
• Investigate various values of the stratification factor β for all networks
• Store the appearance of agents as listeners/speakers, their participation in
overall interactions versus successful meetings and the evolution of the
agent’s inventory size
• Each agent’s inventory is initialized with a fixed number of three opinions
(numbers from 0 to 99)
• These opinions are selected uniformly at random from a bag of opinions to
ensure that each opinion occurs with the same probability
[1] https://github.com/floriangeigl/reputation networks
11

12. http://Learning-Layers-eu
Results
12
Highest convergence rate for 0.0001 < β < 0.0002

13. http://Learning-Layers-eu 13

14. http://Learning-Layers-eu
Results
14

15. http://Learning-Layers-eu 15

16. http://Learning-Layers-eu
Take Away Messages
• Social status strongly influences the opinion
dynamics in a complex and intricate way
• Weakly stratified societies reach consensus at the
highest convergence rate, whereas completely
stratified societies do not reach consensus at all
• The most important issue in this process is
related to low status agents and how their
communication is controlled
16

17. http://Learning-Layers-eu
Future Work
• Engineering consensus building
• Investigate how status and/or network
structure can be adjusted to support the
process
• Datasets with the strong communities where
the consensus reaching is prohibited
17

18. http://Learning-Layers-eu
Thank you for your attention!
Questions?
Ilire Hasani-Mavriqi
ihasani@know-center.at
Knowledge Technologies Institute, KTI
Graz University of Technology
Social Computing Team, Know-Center (Austria)
18