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Naming Games in Social Networks

Naming Games in Social Networks. Qiming Lu Dept. of Physics, Rensselaer György Korniss Dept. of Physics, Rensselaer Boleslaw Szymanski Dept of Computer Science, Rensselaer Funded by NSF & Rensselaer. Language Games, Semiotic Dynamics.

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Naming Games in Social Networks

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  1. Naming Games in Social Networks Qiming LuDept. of Physics, Rensselaer György Korniss Dept. of Physics, Rensselaer Boleslaw Szymanski Dept of Computer Science, Rensselaer Funded by NSF & Rensselaer

  2. Language Games, Semiotic Dynamics • Evolution of “language” in artificial or human agents • Artificial and autonomous software agents or robots bootstraping a shared lexicon without human intervention (Steels, 1995) • Collaborative tagging: human web users spontaneously create loose categorization schemes (“folksonomy”). See, e.g., del.icio.us and www.flickr.com (Golder & Huberman, ‘05; Cattuto et al., ‘05 ) • Can also be used to identify community structures in complex networks Rensselaer Polytechnic Institute

  3. Naming (Language) GamesRules and model description (for a single object) “speaker” “listener” “speaker” “listener” failure oko eta blah kefe oko eta blah blah kefe “speaker” “listener” “speaker” “listener” success oko eta blah blah kefe blah blah Steels (1995) Baronchelli et al. (2005) Rensselaer Polytechnic Institute

  4. Temporal Behavior in NGFully-connected (complete graph) & 2D regular network Total number of words Number of different words Success rate Baronchelli et al. (2005) Rensselaer Polytechnic Institute

  5. ~N1.07 Convergence Time Temporal Behavior in NGRandom Geometric Graphs (Spatial graph: Coarsening) Total number of words Number of different words Success rate Rensselaer Polytechnic Institute Lu. et al., ‘06

  6. Comparison of Average Consensus Times For d<d*, d-dimensional coarsening: 1d-reg 2d-reg ~N2 ~N1.3 d=1: d=2: 2d-RGG FC For d>d*, mean-field-like: ~N0.5 2d-SW-RGG With: Baronchelli et al. ’06Dall’Asta et al. ’06Lu. et al., ‘06 Rensselaer Polytechnic Institute

  7. Naming Game in Social Networks • Prototypical [SW (Watts-Strogatz), SF (Barabási-Albert)] network ensembles have strong self-averaging properties (any single realization almost always resembles a typical one, as opposed to “atypical” but sometimes real-life network topologies) • Many equilibrium and dynamic models on these networks display mean-field features • A more challenging scenario: networks with community structures (Dall’Asta et al.’06) (Gonzalez et. al. ’06) High-school friendship networks from Add Health (Moody 2001) Rensselaer Polytechnic Institute

  8. NG in Social Networkswith strong community structure High-school friendship networks from Add Health (Moody 2001) Rensselaer Polytechnic Institute

  9. NG in Social Networks with strong community structure Number of different word: Nd=3 Nd=2 Nd=1 Rensselaer Polytechnic Institute

  10. NG in Social Networkswith strong community structure High-school friendship networks from Add Health (Moody 2001) Rensselaer Polytechnic Institute

  11. Conclusion • The network structure strongly affects the outcome of the agreement dynamics: • Coarsening  mean-field-like behavior • This simple model can be used to probe and identify the community structure of (social) networks • Q-state Potts model approach: Kumpula et al. 07 • Lu et al. ‘06 http://arxiv.org/abs/cs/0604075 • Will discuss some more details in our poster Rensselaer Polytechnic Institute

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