small worlds in  semantic networks

small worlds in semantic networks PowerPoint PPT Presentation


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Real-Life Networks:Collaboration network for film actorsPower-gridNeural network of worm: C. elegansWWW. This Research:Consider semantic networks:Do semantic networks have similar properties?What network model can predict these properties? . Path Lengths

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small worlds in semantic networks

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1. Small Worlds in Semantic Networks Mark Steyvers Josh Tenenbaum Stanford University

3. Path Lengths & Clustering Watts & Strogatz ’98; Adamic ‘99; Albert, Jeong, & Barabasi, ‘99

4. Erdös-Réyni graphs

5. Path Lengths & Clustering compared with Erdös-Réyni graphs

6. Degree Distribution

7. Semantic Networks Associative Networks WordNet Roget’s Thesaurus

8. Word Association Nelson et al. (1999)

9. WordNet George Miller and colleagues

10. Roget’s Thesaurus (1911)

11. Path-lengths & Clustering

12. Degree Distribution

13. Growing, Scale-Free Networks Barabasi & Albert ‘99

16. Degree vs. Age of Acquisition (rated)

17. Degree vs. Age of Acquisition (objective)

18. Conclusions Semantic networks have following global characteristics: Short path lengths Clustering Power law distribution of the degree of nodes Growth model with preferential attachment describes global characteristics for several real-life networks. Open questions: Can we model memory disorders (e.g. semantic dementia) by deleting nodes and/or connections? can small worlds also occur in featural representations?

19. Frequent words on shortest paths

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