Publication Networks in Regional Science in Europe An Application of Social Network Analysis

1. Publication Networks in Regional Science in EuropeAn Application of Social Network Analysis Gunther Maier

2. The Networks of ERSA • Introduction • Theory and methods • Social Network Analysis • Application of SNA to co-authorship • by country • by city • by person • Based on collaborative work with Jouke van Dijk and Michael Vyborny

3. Introduction „networking“, „clusters“, „agglomeration effects“ are today‘s catchwords in RS • To what extent do regional scientists network? What are their networks? • Academic interest - organizational interest • Spatial dimension • Centers of RS in Europe?

4. Theory and methods • co-authorship • increasing number of co-authored publications • pro: specialization, synergy, output effect (# of publications), consumption effect • contra: compromize on text, transaction costs, free riding

5. Spatial dimension of co-authorship • Little information • Laband and Tollison (2000): working at different locations - increasing over time, females more likely to participate in teamwork, but less likely to co-operate with different locations • more co-authorship in RS? Not confirmed for JEL 900

6. Social network analysis • Technique to identify, analyze, measure and visualize networks • based on graph theory (nodes, links) • new software (Ucinet, Netdraw, Pajek) allows for visualization and interaction

7. Goals of social network analysis • „The main goal of social network analysis is detecting and interpreting patterns of social relations between actors.“ (de Nooy/Mrvar/Batagelj, 2004, S. 5) • The most basic unit of analysis in social network analysis is the dyade, the relation between two actors.

8. Social network analysis • “Social network analysis is motivated by a structural intuition based on ties linking social actors, • It is grounded in systematic empirical data • It draws heavily on graphic imagery, and • It relies on the use of mathematical and/or computational models” • (Freeman, 2004, S. 3)

9. What is a network? • Actors • Relations • Sociogram • Interaction matrix • unimodal, bi-modal

10. Unimodal vs, bi-modal XX‘ X‘X

11. Unimodal vs, bi-modal

12. Graph theory • Nodes • Directed vs. non-directed ties • Binary vs. valued data • Tie strength • Signed graph • Distance • Attributes

13. Centrality vs. Centralization • Degree Centrality • Centralization • Closeness Centrality • Betweenness Centrality

14. Cohesive subgroups • Component • Cut-point • Bridge • K-core • Clique • M-slice

15. Roles • Coordinator • Itinerant broker • Representative • Gatekeeper • Liaison

16. The dataset • CD-ROMs of ERSA-congresses 1998-2003 • all authors and coauthors are considered active participants • information about city/country from conf-vienna databases • advantage: documented activity • disadvantage: not all presented papers, many co-authors not at conference

17. Papers 1657 one more Authors 735 922 one more Cities 620 302 one more Countries 183 119 Analysis of co-authorship • What is collaboration?

18. Analysis of co-authorship

19. Co-authorship networks • Networks of countries • Networks of cities • Networks of people

20. Co-authorship networks / country • 52 countries in database, 37 connected, 15 isolates • (Argentina, Egypt, Estonia, Czech Republic, Hungary, Indonesia, India, Latvia, Lithuania, Slovakia, Luxembourg, Morocco, South Africa, Taiwan and Ukraine) • 2 components • Canada, Croatia, Slovenia • all other countries

21. Co-authorship networks / country

22. Co-authorship networks / city • 429 cities in dataset, 271 connected, 158 isolates • 46 components, 31 connecting only two cities

23. Cities - core component

24. Cities - smaller networks

25. Co-authorship networks / authors

26. Co-authorship networks / authors • 1459 authors in the database • 396 components, almost half link only 2 authors, largest component links 91 authors. • 243 components (61%) link only authors from one city • 330 (83%) link only authors from one country

27. Co-authorship networks / authors • Large or small? • Many or few? • Generate „expected“ • same # of nodes and links • random selection of links • 15500 repetitions

28. More components and smaller components than expected. Largest component only 1/10th of expected links between some nodes are more likely than between others (spatial proximity?) Co-authorship networks / authors

29. Co-authorship networks / authors • Power law? • Observed:ln(S) = 3.05-0.46*ln(N)(r2 = 0.769) • Expected:ln(S) = 3.23-0.68*ln(N)(r2 = 0.397)

30. The Dutch network

31. The Barcelona network

32. The Swiss network

33. The Scandinavian network

34. Other significant components

35. Characteristics of components

36. Summary and conclusions • centers of RS in Europe: • differs between countries, cities, persons • countries: UK, GM, IT, SP, NL - US • cities: Amsterdam, Rome, Milano, Nuremberg, Barcelona, London - Sao Paulo • persons: different networks with different structures - 1 / 2 central figures, core team