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Citation interactions among computer science fields: a quantitative route to the rise and fall of scientific research

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Abstract

In this work, we propose for the first time a suite of metrics that can be used to perform post-hoc analysis of the temporal communities of a large-scale citation network of the computer science domain. Each community refers to a particular research field in this network, and therefore, they act as natural sub-groupings of this network (i.e., ground-truths). The interactions between these ground-truth communities through citations over the real time naturally unfold the evolutionary landscape of the dynamic research trends in computer science. These interactions are quantified in terms of a metric called inwardness that captures the effect of local citations to express the degree of authoritativeness of a community (research field) at a particular time instance. In particular, we quantify the impact of a field, the influence imparted by one field on the other, the distribution of the “star” papers and authors, the degree of collaboration and seminal publications to characterize such research trends. In addition, we tear the data into three subparts representing the continents of North America, Europe and the rest of the world, and analyze how each of them influences one another as well as the global dynamics. We point to how the results of our analysis correlate with the project funding decisions made by agencies like NSF. We believe that this measurement study with a large real-world data is an important initial step towards understanding the dynamics of cluster-interactions in a temporal environment. Note that this paper, for the first time, systematically outlines a new avenue of research that one can practice post community detection.

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Notes

  1. http://arnetminer.org/citation, named as DBLP-Citation-network V4.

  2. http://academic.research.microsoft.com/.

  3. Note that, these results are representative and therefore hold for any reasonable size sampling of the data. The first set represents a period of the most recent 5 years; the second set corresponds to a period of 10 years from the immediate past; the third and fourth sets represent the full data partitioned into two chunks, and the last set presents the results on the entire dataset.

  4. Note that, in this case, the rank \(x\) is higher than rank \(y\) if \(x<y\) conforming to the usual notion of any ranking system.

  5. http://www.nsf.gov/.

References

  • Blei DM, Lafferty JD (2006) Dynamic topic models. Proceedings of the 23rd international conference on Machine learning., International Conference on Machine Learning (ICML) ACM, New York, USA, pp 113–120

  • Booth KS, Lueker GS (1976) Testing for the consecutive ones property, interval graphs, and graph planarity using pq-tree algorithms. Journal of Computer and System Sciences 13(3):335–379

    Article  MathSciNet  MATH  Google Scholar 

  • Bornholdt S, Jensen MH, Sneppen K (2011) Emergence and decline of scientific paradigms. Phys Rev Lett 106(5):058701

    Article  Google Scholar 

  • Boyack KW, Klavans R, Börner K (2005) Mapping the backbone of science. Scientometrics 64(3):351–374

    Article  Google Scholar 

  • Chakraborty T, Kumar S, Reddy MD, Kumar S, Ganguly N, Mukherjee A (2013) Automatic classification and analysis of interdisciplinary fields in computer sciences. In: 2013 ASE/IEEE International Conference on Social Computing (SocialCom), Washington DC, USA pp 180–187

  • Chakraborty T, Sikdar S, Tammana V, Ganguly N, Mukherjee A (2013) Computer science fields as ground-truth communities: their impact, rise and fall. In: IEEE/ACM international conference on advances in social networks analysis and mining (ASONAM), Nigara Falls, Canada pp 426–433

  • Egghe L, Leydesdorff L (2009) The relation between Pearson’s correlation coefficient r and Salton’s cosine measure. J Am Soc Inf Sci Technol 60(5):1027–1036

    Article  Google Scholar 

  • Franceschet M (2010) A comparison of bibliometric indicators for computer science scholars and journals on Web of Science and Google Scholar. Scientometrics 83(1):243–258

    Article  Google Scholar 

  • Garfield E, Sher IH, Torpie RJ (1964) The use of citation data in writing the history of science. Institute for Scientific Information Inc

  • Girvan M, Newman MEJ (2002) Community structure in social and biological networks. Proc Natl Acad Sci US (PNAS) 99(12):7821–7826

    Article  MathSciNet  MATH  Google Scholar 

  • Guimerà R, Uzzi B, Spiro J, Amaral LAN (2005) Team assembly mechanisms determine collaboration network structure and team performance. Science 308(5722):697–702

    Article  Google Scholar 

  • Guns R, Rousseau R (2009) Real and rational variants of the h-index and the g-index. J Informetr 3(1):64–71

    Article  Google Scholar 

  • Hirsch JE (2007) Does the h index have predictive power? Proc Natl Acad Sci US (PNAS) 104(49), 19,193–19,198

    Article  Google Scholar 

  • Hirsch JE (2010) An index to quantify an individual’s scientific research output that takes into account the effect of multiple coauthorship. Scientometrics 85(3):741–754

    Article  Google Scholar 

  • Jin B, Liang L, Rousseau R, Egghe L (2007) The R- and AR-indices: complementing the h-index. Chin Sci Bull 52(6):855–863

    Article  Google Scholar 

  • Kawamae N, Higashinaka R (2010) Trend detection model. In: Proceedings of WWW ’10, ACM, New York, USA, pp 1129–1130

  • Kleinberg J (1999) Authoritative sources in a hyperlinked environment. J ACM 46(5):604–632

    Article  MathSciNet  MATH  Google Scholar 

  • Kuhn TS (1970) The structure of scientific revolutions. University of Chicago Press, Chicago

  • Lee K, Palsetia D, Narayanan R, Patwary MMA, Agrawal A, Choudhary AN (2011) Twitter trending topic classification. In: International conference on data mining (ICDM) Workshops, pp 251–258

  • Leicht EA, Clarkson G, Shedden K (2007) Newman: Large-scale structure of time evolving citation networks. Eur Phys J B 59(1):75–83

    Article  MATH  Google Scholar 

  • Mazloumian A, Eom YH, Helbing D, Lozano S, Fortunato S (2011) How citation boosts promote scientific paradigm shifts and nobel prizes. PLoS ONE 6(5):e18975

    Article  Google Scholar 

  • Pan RK, Sinha S, Kaski K, Saramäki J (2012) The evolution of interdisciplinarity in physics research. Nature Scientific Reports 2(551)

  • Pham MC, Klamma R (2010) The structure of the computer science knowledge network. In: IEEE/ACM international conference on advances in social networks analysis and mining (ASONAM), IEEE Computer Society, Washington, DC, USA, pp 17–24

  • Radicchi F, Castellano C, Cecconi F, Loreto V, Parisi D (2004) Defining and identifying communities in networks. Proc Natl Acad Sci US (PNAS) 101(9):2658

    Article  Google Scholar 

  • Redner S (2005) Citation statistics from 110 years of physical review. Phys Today 58(6):49–54

    Article  Google Scholar 

  • Shaparenko B, Caruana R, Gehrke J, Joachims T (2005) Identifying temporal patterns and key players in document collections. In: IEEE international conference on data mining (ICDM), pp 165–174.

  • Shi X, Tseng BL, Adamic LA (2009) Information diffusion in computer science citation networks. In: international AAAI conference on weblogs and social media (ICWSM), pp 319–322

  • Shibata N, Kajikawa Y, Takeda Y, Matsushima K (2008) Detecting emerging research fronts based on topological measures in citation networks of scientific publications. TECHNOVATION 28(11)

  • de Solla Price DJ (1965) Networks of scientific papers. Science 149(3683):510–515

  • Spiliopoulou M, Ntoutsi I, Theodoridis Y, Schult R (2006) Monic: modeling and monitoring cluster transitions. In: Proceedings of the 12th ACM SIGKDD, New York, USA, pp 706–711

  • Tang J, Zhang J, Yao L, Li J, Zhang L, Su Z (2008) Arnetminer: extraction and mining of academic social networks. In: ACM SIGKDD, pp 990–998

  • Yang J, Leskovec J (2012) Defining and evaluatingnetwork communities based on ground-truth. In: Proceedings of the ICDM’12, Brussels, Belgium, pp 745–754

  • Yang J, Leskovec J (2013) Overlapping community detection at scale: a nonnegative matrix factorization approach. In: international conference on web search and data mining (WSDM), pp 587–596

  • Zhao Q, Bhowmick SS, Zheng X, Yi K (2008) Characterizing and predicting community members from evolutionary and heterogeneous networks. In: ACM international conference on information and knowledge management (CIKM), pp 309–318

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Correspondence to Tanmoy Chakraborty.

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The first author is supported by the Google India PhD fellowship Grant in Social Computing.

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Chakraborty, T., Sikdar, S., Ganguly, N. et al. Citation interactions among computer science fields: a quantitative route to the rise and fall of scientific research. Soc. Netw. Anal. Min. 4, 187 (2014). https://doi.org/10.1007/s13278-014-0187-3

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