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In765 Knowledge Networks: A Structural Study of Networks

In765 Knowledge Networks: A Structural Study of Networks. Judith Molka-Danielsen Molde University College j.molka-danielsen@himolde.no http://home.himolde.no/~molka 2005. Types of Networks. Why Study Networks? Research Areas.

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In765 Knowledge Networks: A Structural Study of Networks

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  1. In765 Knowledge Networks:A Structural Study of Networks Judith Molka-Danielsen Molde University College j.molka-danielsen@himolde.no http://home.himolde.no/~molka 2005

  2. Types of Networks..

  3. Why Study Networks? Research Areas • Availability and vulnerability of services: electric, telephone, air connections, etc. • Preventing or stopping of viruses on data networks. • The importance of weak ties: connectivness to the core, finding a job, finding a web page. • The characterization of network structure and the role of hubs in the spreading an idea, or proliferation of a product, and managing organizations.

  4. Former Research • Random Network Theory –Erdös & Rényi (1960) • Six Degrees of Separation –S.Milgram (1967) • Cluster Coefficient –Small Worlds – Watts & Strogatz (1998) • Hubs and Scale Free Networks – Albert, Jeong, & Barabási (1999) • Hubs in Social Networks – Malcolm Gladwell (2000)

  5. Random Networks Erdös-Rényi model(1960) Connect with probability p Pál Erdös(1913-1996) p=1/6 N=10 k ~ 1.5 Poisson distribution - Democratic - Random

  6. Six Degrees of Separation Nodes: individuals Links: social relationship (family/work/friendship/etc.) S. Milgram (1967) Six Degrees of Separation John Guare(1980) Social networks: Many individuals with diversesocial interactions between them.

  7. Cluster Coefficient Clustering: My friends will likely know each other! Probability to be connected C»p # of links between 1,2,…n neighbors C = n(n-1)/2 Cfriends= 15/ [6(5)/2] = 100%

  8. Hubs in Networks • 200 million searches each day • More than 2300 searches per second • In 88 languages • 3.2 billion web pages indexed. • 10 000 super computers perform the searches.

  9. Do we find Hubs in Social Networks? Yes. • Most influencial • Access to the most information • Impacts others decisions most • Have the most power

  10. Bjørnstjerne Bjørnsons Vei Alme Jørund Andenes Aud Andestad Reidar Bakke Gerd Inger Bergseth Egil Bergtun Lill Eldrid Bjøringsøy Karl Magnar Bjørkly Jorunn Bjørkly Åsa Bjordal Bjørnebo Solveig Randi Midtbø Broks Vivi-Annie Brokstad Jon Drageseth Dagfinn Dyrli Janne Merete Døving Ellen Eilertsen Gudny Flø Jorunn Marie Fylling Lars Kristen Tovan Gjære Arne Gjære Guro Wiersholm Gjære Vibeke Wiersholm Grønbugt Rutt Grønset Erling Rune Gudbrandsen Åste Einbu Gøncz Geir Janos Göncz Arne Hansen Helge Hansen Sissel Helde Marit Illøkken Henriksen Line Hjelmsøt Maria Hoem Jermund Hofset Siv Jenset Grete Jenset Torbjørn Jordet Birgit Kanestrøm Andreas Julshamn … Who do you know?(similar to a study by Malcolm Gladwell, 2000)

  11. Who do you know?: survey to faculty A B A = number of persons known on the list. B = number of persons (nodes) that person A knows. A B gruppert 22

  12. Who do you know?: survey to students A = number of persons known on the list. A B B = number of persons (nodes) that person A knows. A B 48 gruppert

  13. Scale Free Networks and Power Lawsby Albert, Jeong, Barabasi.

  14. Collaboration Among Researchers Networks have diverse nodes and links are -phone lines -TV cables -EM waves -co-authorship -computers -routers -satellites -researchers

  15. Unique co-author link distribution – researchers represented individually

  16. Unique Co-Authors versus Publications

  17. Average # of Co-Authors versus Publications

  18. Informatics Institute Cluster: researchers and co-author links

  19. Health Institute Cluster: researchers and co-author links

  20. Social Sciences Institute: researchers and co-author links

  21. Economics/Logistics Cluster: researchers and co-author links

  22. Economics Institute Cluster: researchers and co-author links

  23. Connected Network Tree of researchers and co-author links

  24. Conclusions • Network of researchers at HSM is a Scale Free network. (existance of hubs, clustering coeffiencient) • Co-authors are not chosen randomly. • Co-authorship & Publication count: (cannot claim causality) • Average # of co-author per paper is the same regardless of the total # of publications per author. (does not help) • Average # of unique associations is related to a total # of publications per author. (helps) • Role of “connectors” (nodes with a high # of external links) are important • They often have high publication counts. • They have more external contacts. • They are more likely to hold a joint appointment (again not causal).

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