A Novel Comprehensive Index of Network Position and Node Characteristics in Knowledge Networks: Ego Network Quality

  • Tamás Sebestyén
  • Attila VargaEmail author
Part of the Advances in Spatial Science book series (ADVSPATIAL)


While developing the comprehensive index of Ego Network Quality (ENQ) Sebestyén and Varga (Ann Reg Sci, doi:10.1007/s00168-012-0545-x, 2013) integrates techniques mainly applied in a-spatial studies with solutions implemented in spatial analyses. Following the theory of innovation they applied a systematic scheme for weighting R&D in partner regions with network features frequently appearing in several (mostly non-spatial) studies. The resulting ENQ index thus reflects both network position and node characteristics in knowledge networks. Applying the ENQ index in an empirical knowledge production function analysis Sebestyén and Varga (Ann Reg Sci, doi: 10.1007/s00168-012-0545-x, 2013) demonstrate the validity of ENQ in measuring interregional knowledge flow impacts on regional knowledge generation. The aim of this chapter is twofold. First we show that ENQ is an integrated measure of network position and node characteristics very much resembling to the solution applied in the well-established index of eigenvector centrality. Second, we test the robustness of the weighting schemes in ENQ via simulation and empirical regression analyses.


Cluster Coefficient Knowledge Level Preferential Attachment Knowledge Network Average Path Length 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The research leading to this paper has received funding from the Hungarian Academy of Sciences (n° 14121: MTA-PTE Innovation and Economic Growth Research Group) and OTKA (OTKA-K101160). The authors also wish to express their thanks to the useful comments by Nicolas Carayol, Claude Raynaut, Frank van Oort and Mario Maggioni.


  1. Anselin L, Varga A, Acs Z (1997) Local geographic spillovers between university research and high technology innovations. J Urban Econ 42:422–448CrossRefGoogle Scholar
  2. Barabási AL (2003) Linked: how everything is connected to everything else what it means for business, science and everyday life. Penguing Group, New YorkGoogle Scholar
  3. Barabási AL, Albert R (1999) Emergence of scaling in random networks. Science 286:509–512CrossRefGoogle Scholar
  4. Bonacich P (1972) Factoring and weighting approaches to clique identification. J Math Sociol 2:113–120CrossRefGoogle Scholar
  5. Bonacich P (2007) Some unique properties of eigenvector centrality. Soc Netw 29:555–564CrossRefGoogle Scholar
  6. Burt RS (1992) Structural holes. Harvard University Press, Cambridge, MAGoogle Scholar
  7. Burt RS, Hogarth RM, Michaud C (2000) The social capital of French and American managers. Organ Sci 11:123–147CrossRefGoogle Scholar
  8. Burton P, Yu W, Prybutok V (2010) Social network position and its relationship to performance of IT professionals (Report). Informing Science: the International Journal of an Emerging Transdiscipline. Informing Science Institute. HighBeam ResearchGoogle Scholar
  9. Coleman JS (1986) Social theory, social research, and a theory of action. Am J Sociol 91:1309–1335CrossRefGoogle Scholar
  10. Cross R, Cummings JN (2004) Tie and network correlates of individual performance in knowledge-intensive work. Acad Manage J 47:928–937CrossRefGoogle Scholar
  11. Csermely P (2006) Weak links: stabilizers of complex systems from proteins to social networks. Springer, BerlinGoogle Scholar
  12. Diez R (2002) Metropolitan innovation systems – a comparison between Barcelona, Stockholm, and Vienna. Int Reg Sci Rev 25:63–85CrossRefGoogle Scholar
  13. Donckels R, Lambrecht J (1997) The network position of small businesses: an explanatory model. J Small Bus Manage 35(2):13–26Google Scholar
  14. Erdős P, Rényi A (1959) On random graphs I. Publ Math 6:290–297Google Scholar
  15. Fischer M, Varga A (2002) Technological innovation and interfirm cooperation. An exploratory analysis using survey data from manufacturing firms in the metropolitan region of Vienna. Int J Technol Manage 24:724–742CrossRefGoogle Scholar
  16. Godsil C, Royle GF (2001) Algebraic graph theory. Graduate texts in mathematics. Springer, New YorkGoogle Scholar
  17. Hopp WJ, Iravani S, Liu F, Stringer MJ (2010) The impact of discussion, awareness, and collaboration network position on research performance of engineering school faculty. Ross School of Business paper no. 1164Google Scholar
  18. Jones C (1995) R&D-based models of economic growth. J Polit Econ 103(4):759–784CrossRefGoogle Scholar
  19. Kretschmer H (2004) Author productivity and geodesic distance in bibliographic co-authorship networks, and visibility on the web. Scientometrics 60(3):409–420CrossRefGoogle Scholar
  20. Lundvall BA (1992) National systems of innovation. Pinter Publishers, LondonGoogle Scholar
  21. Maggioni M, Uberti T (2011) Networks and geography in the economics of knowledge flows. Qual Quant 45(5):1031–1051Google Scholar
  22. Nelson RR (ed) (1993) National innovation systems: a comparative analysis. Oxford University Press, OxfordGoogle Scholar
  23. OECD (2009) REGPAT database. Oct 2009, ParisGoogle Scholar
  24. Ponds R, van Oort F, Frenken K (2010) Innovation, spillovers and university–industry collaboration: an extended knowledge production function approach. J Econ Geogr 10:231–255CrossRefGoogle Scholar
  25. Powell WW, Koput KW, Smith-Doerr L, Owen-Smith J (1999) Network position and firm performance: organizational returns to collaboration in the biotechnology industry. In: Andrews SB, Knoke D (eds) Networks in and around organizations. JAI Press, GreenwichGoogle Scholar
  26. Romer PM (1990) Endogenous technological change. J Polit Econ 5(98):S71–S102CrossRefGoogle Scholar
  27. Rumsey-Wairepo A (2006) The association between co-authorship network structures and successful academic publishing among higher education scholars. Brigham Young UniversityGoogle Scholar
  28. Salmenkaita JP (2004) Intangible capital in industrial research: effects of network position on individual inventive productivity. In: Bettis R (ed) Strategy in transition. Blackwell, Malden, pp 220–248Google Scholar
  29. Sebestyén T (2011) Knowledge networks and economic performance. Approaches for modeling and empirical analysis. VDM Verlag, SaarbrückenGoogle Scholar
  30. Sebestyén T, Varga A (2013) Research productivity and the quality of interregional knowledge networks. Ann Reg Sci. doi: 10.1007/s00168-012-0545-x Google Scholar
  31. Tsai W (2001) Knowledge transfer in intraorganizational networks: effects of network position and absorptive capacity on business unit innovation and performance. Acad Manage J 44(5):996–1004CrossRefGoogle Scholar
  32. Varga A (2000) Local academic knowledge transfers and the concentration of economic activity. J Reg Sci 40(2):289–309CrossRefGoogle Scholar
  33. Varga A (2006) The spatial dimension of innovation and growth: empirical research methodology and policy analysis. Eur Plann Stud 9:1171–1186CrossRefGoogle Scholar
  34. Varga A, Pontikakis D, Chorafakis G (2013) Metropolitan Edison and cosmopolitan Pasteur? Agglomeration and interregional research network effects on European R&D productivity. J Econ Geogr 51(1):155–189Google Scholar
  35. Wasserman S, Faust K (1994) Social network analysis – methods and application. Cambridge University Press, Cambridge, UKCrossRefGoogle Scholar
  36. Watts DJ, Strogatz SH (1998) Collective dynamics of ‘small-world’ networks. Nature 393(6684):409–410CrossRefGoogle Scholar
  37. Zaheer A, Bell GG (2005) Benefiting from network position: firm capabilities, structural holes and performance. Strateg Manage J 26:809–825CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  1. 1.MTA-PTE Innovation and Economic Growth Research Group and Department of Economics and Regional Studies, Faculty of Business and EconomicsUniversity of PécsPécsHungary
  2. 2.Department of Economics and Regional Studies and MTA-PTE Innovation and Economic Growth Research GroupUniversity of PécsPécsHungary

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