Social Network Analysis and Mining

, Volume 1, Issue 4, pp 321–340 | Cite as

Development of computer science disciplines: a social network analysis approach

  • Manh Cuong PhamEmail author
  • Ralf Klamma
  • Matthias Jarke
Original Article


In contrast to many other scientific disciplines, computer science considers conference publications. Conferences have the advantage of providing fast publication of papers and of bringing researchers together to present and discuss the paper with peers. Previous work on knowledge mapping focused on the map of all sciences or a particular domain based on ISI published Journal Citation Report (JCR). Although this data cover most of the important journals, it lacks computer science conference and workshop proceedings, which results in an imprecise and incomplete analysis of the computer science knowledge. This paper presents an analysis on the computer science knowledge network constructed from all types of publications, aiming at providing a complete view of computer science research. Based on the combination of two important digital libraries (DBLP and CiteSeerX), we study the knowledge network created at journal/conference level using citation linkage, to identify the development of sub-disciplines. We investigate the collaborative and citation behavior of journals/conferences by analyzing the properties of their co-authorship and citation subgraphs. The paper draws several important conclusions. First, conferences constitute social structures that shape the computer science knowledge. Second, computer science is becoming more interdisciplinary. Third, experts are the key success factor for sustainability of journals/conferences.


Digital Library Cluster Coefficient Betweenness Centrality Cosine Similarity Citation Network 
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.



This work has been supported by the Graduiertenkolleg (GK) “Software for mobile communication system”, RWTH Aachen University, the BIT Research School by RWTH Aachen University and the University of Bonn, and the EU FP7 IP ROLE. We would like to thank our colleagues for the fruitful discussions.


  1. Bollen J, Van de Sompel H, Hagberg A, Bettencourt L, Chute R, Rodriguez MA, Balakireva L (2009) Clickstream data yields high-resolution maps of science. PLoS ONE 4(3):e4803+. doi: 10.1371/journal.pone.0004803
  2. Bollen J, de Sompel HV, Hagberg AA, Chute R (2009) A principal component analysis of 39 scientific impact measures. CORR abs/0902.2183Google Scholar
  3. Boyack KW, Börner K, Klavans R (2007) Mapping the structure and evolution of chemistry research. In: Torres-Salinas D, Moed H (eds) Proceedings of the 11th international conference of scientometrics and informetrics, p 112123Google Scholar
  4. Boyack KW, Klavans R, Börner K (2005) Mapping the backbone of science. Scientometrics 64(3):351–374. doi: 10.1007/s11192-005-0255-6 Google Scholar
  5. Brin S, Page L (1998) The anatomy of a large-scale hypertextual web search engine. Comput Netw ISDN Syst 30(1–7):107–117. doi: 10.1016/S0169-7552(98)00110-X Google Scholar
  6. Chen J, Konstan JA (2009) Conference paper selectivity and impact. Commun ACM 53:79–83. doi: 10.1145/1743546.1743569 Google Scholar
  7. Clauset A, Newman MEJ, Moore C (2004) Finding community structure in very large networks. Phys Rev E 70:066111. doi: 10.1103/PhysRevE.70.066111 Google Scholar
  8. Coleman JS (1988) Social capital in the creation of human capital. Am J Sociol 94:S95–S120. Google Scholar
  9. Ding Y, Chowdhury G, Foo S (2000) Journal as markers of intellectual space: journal co-citation analysis of information retrieval area. Scientometrics 47(1):55–73CrossRefGoogle Scholar
  10. Fortnow L (2009) Viewpoint: time for computer science to grow up. Commun ACM 52:33–35. doi: 10.1145/1536616.1536631 Google Scholar
  11. Freyne J, Coyle L, Smyth B, Cunningham P (2010) Relative status of journal and conference publications in computer science. Commun ACM 53:124–132. doi: 10.1145/1839676.1839701 Google Scholar
  12. Fruchterman TMJ, Reingold EM (1991) Graph drawing by force-directed placement. Softw Pract Exper 21(11):1129–1164. doi: 10.1002/spe.4380211102 Google Scholar
  13. Gilbert F, Simonetto P, Zaidi F, Jourdan F, Bourqui R (2010) Communities and hierarchical structures in dynamic social networks: analysis and visualization. Soc Netw Anal Min, pp 1–13. doi: 10.1007/s13278-010-0002-8
  14. Granovetter M (1983) The strength of weak ties: a network theory revisited. Soc Theory 1:201–233CrossRefGoogle Scholar
  15. Han H, Giles CL, Zha H, Li C, Tsioutsiouliklis K (2004) Two supervised learning approaches for name disambiguation in author citations. In: JCDL, pp 296–305Google Scholar
  16. Hirsch JE (2005) An index to quantify an individual’s scientific research output. Proc Nat Acad Sci 102(46):16569–16572. doi: 10.1073/pnas.0507655102
  17. Huang J, Ertekin S, Giles CL (2006) Efficient name disambiguation for large-scale databases. In: PKDD, pp 536–544Google Scholar
  18. Kienle A, Wessner M (2005a) Our way to Taipei: an analysis of the first ten years of the cscl community. In: Proceedings of th 2005 conference on computer support for collaborative learning: learning 2005: the next 10 years!, CSCL’05. International Society of the Learning Sciences, Chicago, pp 262–271.
  19. Kienle A, Wessner M (2005b) Principles for cultivating scientific communities of practice. In: Besselaar P, Michelis G, Preece J, Simone C (eds) Communities Technol. Springer, Netherlands, pp 283–299. doi: 10.1007/1-4020-3591-8_15
  20. Kienle A, Wessner M (2006) Analysing and cultivating scientific communities of practice. Int J Web Based Communities 2:377–393. doi: 10.1504/IJWBC.2006.011765. Google Scholar
  21. Klavans R, Boyack KW (2006) Identifying a better measure of relatedness for mapping science. J Am Soc Inf Sci Technol 57(2):251–263. doi: 10.1002/asi.v57 Google Scholar
  22. Kumar M (2009) Evaluating scientists: citations, impact factor, h-index, online page hits and what else? IETE Tech Rev 26(3):165–168. doi: 10.4103/0256-4602.50699 Google Scholar
  23. Lambiotte R, Panzarasa P (2009) Communities, knowledge creation, and information diffusion. J Informetr 3(3):180–190. doi: 10.1016/j.joi.2009.03.007. Science of Science: Conceptualizations and Models of ScienceGoogle Scholar
  24. Lee D, On BW, Kang J, Park S (2005) Effective and scalable solutions for mixed and split citation problems in digital libraries. In: IQIS, pp 69–76Google Scholar
  25. Ley M (2009) Dblp—some lessons learned. PVLDB 2(2):1493–1500MathSciNetGoogle Scholar
  26. Leydesdorff L (2004) Clusters and maps of science journals based on bi-connected graphs in the journal citation reports. J Doc 60(4):317. Google Scholar
  27. Leydesdorff L (2004) Top-down decomposition of the journal citation reportof the social science citation index: graph- and factor-analytical approaches. Scientometrics 60(2):317. Google Scholar
  28. Leydesdorff L (2007) Betweenness centrality as an indicator of the interdisciplinarity of scientific journals. J Am Soc Inf Sci Technol. 58(9):1303–1319. doi: 10.1002/asi.20614 Google Scholar
  29. Mani D, Knoke D (2011) On intersecting ground: the changing structure of us corporate networks. Soc Netw Anal Min 1:43–58. doi: 10.1007/s13278-010-0013-5
  30. McCain KW (1998) Neural networks research in context: a longitudinal journal cocitation analysis of an emerging interdisciplinary field. Scientometrics 5(5):389–410CrossRefGoogle Scholar
  31. McCallum A, Nigam K, Ungar LH (2000) Efficient clustering of high-dimensional data sets with application to reference matching. In: KDD’00: proceedings of the sixth ACM SIGKDD international conference on knowledge discovery and data mining, pp 169–178. ACM, New York. doi: 10.1145/347090.347123
  32. Meyer B, Choppy C, Staunstrup J, van Leeuwen J (2009) Viewpoint: research evaluation for computer science. Commun ACM 52:31–34. doi: 10.1145/1498765.1498780 Google Scholar
  33. Morris TA, McCain KW (1998) The structure of medical informatics journal literature. J Am Med Inform Assoc 5(5):448–566CrossRefGoogle Scholar
  34. Moya-Anegón F, Vargas-Quesada B, Herrero-Solana V, Chinchilla-Rodríguez Z, Corera-Álvarez E, noz Fernández FJM (2004) A new technique for building maps of large scientific domains based on the co-citation of classes and categories. Scientometrics 61(1):129–145. Google Scholar
  35. Newman MEJ (2004) Fast algorithm for detecting community structure in networks. Phys Rev E 69:066133.
  36. Newman MEJ (2006) Modularity and community structure in networks. Proc Natl Acad Sci USA 103:8577. doi: 10.1073/pnas.0601602103
  37. Newman MEJ, Girvan M (2004) Finding and evaluating community structure in networks. Phys Rev E 69, 026,113.
  38. Pereira DA, Ribeiro-Neto BA, Ziviani N, Laender AHF, Gonçalves MA, Ferreira AA (2009) Using web information for author name disambiguation. In: JCDL, pp 49–58Google Scholar
  39. Pham M, Klamma R (2010) The structure of the computer science knowledge network. In: 2010 International conference on advances in social networks analysis and mining (ASONAM), pp 17 –24. doi: 10.1109/ASONAM.2010.58
  40. Seglen PO (1997) Why the impact factor of journals should not be used for evaluating research. BMJ 314:498–502Google Scholar
  41. Shi X, Leskovec J, McFarland DA (2010) Citing for high impact. In: Proceedings of the 10th annual joint conference on digital libraries, JCDL’10, pp 49–58. ACM, New York. doi: 10.1145/1816123.1816131
  42. Staff C (2009) Pay for editorial independence. Commun ACM 52:6–7. doi: 10.1145/1592761.1592764 Google Scholar
  43. Treeratpituk P, Giles CL (2009) Disambiguating authors in academic publications using random forests. In: JCDL, pp 39–48Google Scholar
  44. Tsay MY, Xu H, wen Wu C (2003) Journal co-citation analysis of semiconductor literature. Scientometrics 57(1):7–25CrossRefGoogle Scholar
  45. Vardi MY (2009) Conferences vs. journals in computing research. Commun ACM 52:5–5. doi: 10.1145/1506409.1506410 Google Scholar
  46. Wasserman S, Faust K (1995) Social network analysis: methods and applications (structural analysis in the social sciences). Cambridge University Press, Cambridge.
  47. Zhuhadar L, Nasraoui O, Wyatt R, Yang R (2010) Visual knowledge representation of conceptual semantic networks. Social Netw Anal Min pp 1–11. doi: 10.1007/s13278-010-0008-2

Copyright information

© Springer-Verlag 2011

Authors and Affiliations

  1. 1.Information Systems and Database Technology RWTH Aachen University, AachenAachenGermany

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