the influence of social status on consensus building in collaboration networks
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http://Learning-Layers-euhttp://Learning-Layers-eu
Learning LayersScaling 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
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Austrian Science Fund: P 24866-N15
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
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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
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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
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Naming Game Meeting
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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)
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The emergence of social classes based on the stratification factor β
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β = 0, psl is always 1 -> egalitarian society
β = 0.0001, psl decays [0,1] –> ranked society
β = 1, psl is 0 –> stratified society
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Datasets
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• 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)
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Datasets – distribution of reputation scores
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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
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Results
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Highest convergence rate for 0.0001 < β < 0.0002
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Results
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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
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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
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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)
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