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Abstract

Delineation of scientific domains (fields, areas of science) is a preliminary task in bibliometric studies at the mesolevel, far from straightforward in domains with high multidisciplinarity, variety, and instability. The Sect. 2.2 shows the connection of the delineation problem to the question of disciplines versus invisible colleges, through three combinable models: ready-made classifications of science, classical information-retrieval searches, mapping and clustering. They differ in the role and modalities of supervision. The Sect. 2.3 sketches various bibliometric techniques against the background of information retrieval ( ), data analysis, and network theory, showing both their power and their limitations in delineation processes. The role and modalities of supervision are emphasized. The Sect. 2.4 addresses the comparison and combination of bibliometric networks (actors, texts, citations) and the various ways to hybridize. In the Sect. 2.5, typical protocols and further questions are proposed.

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References

  1. A. Comte: Cours de Philosophie Positive, Vol. 1 (Rouen Frères, Paris 1830)

    Google Scholar 

  2. R.K. Merton: Science and technology in a democratic order, J. Leg. Political Sociol. 1(1), 115–126 (1942)

    Google Scholar 

  3. R.K. Merton: The Sociology of Science: Theoretical and Empirical Investigations (Univ. Chicago Press, Chicago 1973)

    Google Scholar 

  4. T.S. Kuhn: The Structure of Scientific Revolutions, 2nd edn. (Univ. Chicago Press, Chicago 1970)

    Google Scholar 

  5. H.M. Collins, S. Yearley: Epistemological chicken. In: Science as Practice and Culture, ed. by A. Pickering (Univ. Chicago Press, Chicago 1992) pp. 301–326

    Google Scholar 

  6. B. Barnes, D. Bloor, J. Henry: Scientific Knowledge: A Sociological Analysis (Univ. Chicago Press, Chicago 1996)

    Google Scholar 

  7. D. Bloor: Knowledge and Social Imagery (Routledge Kegan Paul, London 1976)

    Google Scholar 

  8. K.D. Knorr-Cetina: Scientific communities or transepistemic arenas of research? A Critique of quasi-economic models of science, Soc. Stud. Sci. 12(1), 101–130 (1982)

    Article  Google Scholar 

  9. M.J. Mulkay, G.N. Gilbert, S. Woolgar: Problem areas and research networks in science, Sociology 9(2), 187–203 (1975)

    Article  Google Scholar 

  10. M. Serres: La Traduction, Hermès III, Collection ‘Critique' (Les Éditions de Minuit, Paris 1974)

    Google Scholar 

  11. B. Latour, S. Woolgar: Laboratory Life: The Social Construction of Scientific Facts (SAGE, Beverly Hills 1979)

    Google Scholar 

  12. M. Callon, B. Latour: Unscrewing the big leviathan: How actors macro-structure reality and how sociologists help them to do so. In: Advances in Social Theory and Methodology: Toward an Integration of Mirco- and Macro-Sociologies, ed. by K. Knorr-Cetina, A.V. Cicourel (Routledge Kegan Paul, London 1981) pp. 277–303

    Google Scholar 

  13. J. Law, J. Hassard: Actor Network Theory and After (Blackwell, Oxford 1999)

    Google Scholar 

  14. T. Lenoir: Instituting Science: The Cultural Production of Scientific Disciplines (Stanford Univ. Press, Stanford 1997)

    Google Scholar 

  15. V. DiRita: Microbiology is an integrative field, so why are we a divided society?, Microbe Mag. 8(10), 384–385 (2013)

    Article  Google Scholar 

  16. A. Casadevall, F.C. Fang: Field science—The nature and utility of scientific fields, mBio 6(5), e01259–15 (2015)

    Google Scholar 

  17. J. Piaget: L'épistémologie des relations interdisciplinaires. In: Interdisciplinarity: Problems of Teaching and Research in Universities, ed. by L. Apostel, G. Berger, A. Briggs, G. Michaud (OECD, Paris 1972) pp. 127–140

    Google Scholar 

  18. D.J.D. Price, D.D. Beaver: Collaboration in an invisible college, Am. Psychol. 21(11), 1011–1018 (1966)

    Google Scholar 

  19. D. Crane: Invisible Colleges: Diffusion of Knowledge in Scientific Communities (Chicago Univ. Press, Chicago 1972)

    Google Scholar 

  20. D.E. Chubin: Beyond invisible colleges: Inspirations and aspirations of post-1972 social studies of science, Scientometrics 7(3–6), 221–254 (1985)

    Article  Google Scholar 

  21. A. Zuccala: Modeling the invisible college, J. Am. Soc. Inf. Sci. Technol. 57(2), 152–168 (2005)

    Article  Google Scholar 

  22. J. Gläser, G. Laudel: Integrating scientometric indicators into sociological studies: Methodical and methodological problems, Scientometrics 52(3), 411–434 (2001)

    Article  Google Scholar 

  23. P.M. Haas: Introduction: Epistemic communities and international policy coordination, Int. Organization 46(1), 1–35 (1992)

    Article  Google Scholar 

  24. É. Wenger: Communities of Practice: Learning, Meaning, and Identity (Cambridge Univ. Press, New York 1998)

    Book  Google Scholar 

  25. R.P. Smiraglia: Domain analysis of domain analysis for knowledge organization: Observations on an emergent methodological cluster, Knowl. Organ. 42(8), 602–611 (2015)

    Article  Google Scholar 

  26. J. Gläser, A. Scharnhorst, W. Glänzel: Same data—Different results? Towards a comparative approach to the identification of thematic structures in science, Scientometrics 111(2), 979–979 (2017)

    Article  Google Scholar 

  27. C.R. Sugimoto, S. Weingart: The kaleidoscope of disciplinarity, J. Documentation 71(4), 775–794 (2015)

    Article  Google Scholar 

  28. R. Todorov: Representing a scientific field: A bibliometric approach, Scientometrics 15(5/6), 593–605 (1989)

    Article  Google Scholar 

  29. R.J.W. Tijssen: A quantitative assessment of interdisciplinary structures in science and technology: Co-classification analysis of energy research, Res. Policy 21(1), 27–44 (1992)

    Article  Google Scholar 

  30. C.S. Wagner: The New Invisible College: Science for Development (Brookings Institution, Washington 2008)

    Google Scholar 

  31. A. Suominen, H. Toivanen: Map of science with topic modeling: Comparison of unsupervised learning and human-assigned subject classification, J. Assoc. Inf. Sci. Technol. 67(10), 2464–2476 (2016)

    Article  Google Scholar 

  32. E.C.M. Noyons, A.F.J. van Raan: Monitoring scientific developments from a dynamic perspective: Self-organized structuring to map neural network research, J. Am. Soc. Inf. Sci. 49(1), 68–81 (1998)

    Google Scholar 

  33. M. Zitt, E. Bassecoulard: Delineating complex scientific fields by an hybrid lexical-citation method: An application to nanosciences, Inf. Process. Manag. 42(6), 1513–1531 (2006)

    Article  Google Scholar 

  34. J.T. Klein: Interdisciplinarity: History, Theory, and Practice (Wayne State Univ. Press, Detroit 1990)

    Google Scholar 

  35. B.C.K. Choi, A.W.P. Pak: Multidisciplinarity, interdisciplinarity and transdisciplinarity in health research, services, education and policy: 1. Definitions, objectives, and evidence of effectiveness, Clin. Investig. Med. 29(6), 351–364 (2006)

    Google Scholar 

  36. T. Jahn, M. Bergmann, F. Keil: Transdisciplinarity: Between mainstreaming and marginalization, Ecol. Econ. 79, 1–10 (2012)

    Article  Google Scholar 

  37. A.W. Russell, F. Wickson, A.L. Carew: Transdisciplinarity: Context, contradictions and capacity, Futures 40(5), 460–472 (2008)

    Article  Google Scholar 

  38. J.T. Klein: Evaluation of interdisciplinary and transdisciplinary research, Am. J. Prev. Med. 35(2), S116–S123 (2008)

    Article  Google Scholar 

  39. T.R. Miller, T.D. Baird, C.M. Littlefield, G. Kofinas, F.S. Chapin III, C.L. Redman: Epistemological pluralism: Reorganizing interdisciplinary research, Ecol. Soc. 13(2), 46 (2008)

    Article  Google Scholar 

  40. A. Yegros-Yegros, I. Rafols, P. D'Este: Does interdisciplinary research lead to higher citation impact? The different effect of proximal and distal interdisciplinarity, PLOS ONE 10(8), e0135095 (2015)

    Article  Google Scholar 

  41. G.E.A. Solomon, S. Carley, A.L. Porter: How multidisciplinary are the multidisciplinary journals science and nature?, PLOS ONE 11(4), e0152637 (2016)

    Article  Google Scholar 

  42. C.R. Sugimoto, N. Robinson-Garcia, R. Costas: Towards a global scientific brain: Indicators of researcher mobility using co-affiliation data. In: OECD Blue Sky III Forum on Science and Innovation Indicators, ed. by M. Feldman, S. Nagaoka, L. Soete, A. Jaffe, M. Salazar, R. Veugelers (OECD, Paris 2016)

    Google Scholar 

  43. M. Bordons, F. Morillo, I. Gómez: Analysis of cross-disciplinary research through bibliometric tools. In: Handbook of Quantitative Science and Technology Research: The Use of Publication and Patent Statistics in Studies of S&T Systems, ed. by H.F. Moed, W. Glänzel, U. Schmoch (Springer, Dordrecht 2004) pp. 437–456

    Google Scholar 

  44. G. Pinski, F. Narin: Citation influence for journal aggregates of scientific publications: Theory, with application to the literature of physics, Inf. Process. Manag. 12(5), 297–312 (1976)

    Article  Google Scholar 

  45. E.J. Rinia, T.N. van Leeuwen, E.E.W. Bruins, H.G. van Vuren, A.F.J. van Raan: Measuring knowledge transfer between fields of science, Scientometrics 54(3), 347–362 (2002)

    Article  Google Scholar 

  46. E. Bassecoulard, M. Zitt: Patents and publications: The lexical connection. In: Handbook of Quantitative Science and Technology Research: The Use of Publication and Patent Statistics in Studies of S&T Systems, ed. by H.F. Moed, W. Glänzel, U. Schmoch (Springer, Dordrecht 2004) pp. 665–694

    Google Scholar 

  47. K. Börner, R. Klavans, M. Patek, A.M. Zoss, J.R. Biberstine, R.P. Light, V. Larivière, K.W. Boyack: Design and update of a classification system: The UCSD map of science, PLoS ONE 7(7), e39464 (2012)

    Article  Google Scholar 

  48. K.W. Boyack, R. Klavans: The structure of science. In: Places and Spaces: Mapping Science—1st Iteration (2005): The Power of Maps, ed. by K. Börner, D. MacPherson (scimaps.org, Indiana 2005)

    Google Scholar 

  49. A. Stirling: A general framework for analysing diversity in science, technology and society, J. R. Soc. Interface 4(15), 707–719 (2007)

    Article  Google Scholar 

  50. D. Hicks: Limitations and more limitations of co-citation analysis/bibliometric modelling: A reply to Franklin, Soc. Stud. Sci. 18(2), 375–384 (1988)

    Article  Google Scholar 

  51. H.F. Moed: Citation Analysis in Research Evaluation, Information Science and Knowledge Management, Vol. 9 (Springer, Dordrecht 2005)

    Google Scholar 

  52. A.F.J. van Raan, T.N. van Leeuwen, M.S. Visser: Severe language effect in university rankings: Particularly Germany and France are wronged in citation-based rankings, Scientometrics 88(2), 495–498 (2011)

    Article  Google Scholar 

  53. L. Soete, S. Schneegans, D. Eröcal, B. Angathevar, R. Rasiah: A world in search of an effective growth strategy. In: UNESCO Science Report: Towards 2030, UNESCO Reference Works, ed. by S. Schneegans (UNESCO, Paris 2015) pp. 20–55

    Google Scholar 

  54. J. Freyne, L. Coyle, B. Smyth, P. Cunningham: Relative status of journal and conference publications in computer science, Communications ACM 53(11), 124–132 (2010)

    Article  Google Scholar 

  55. A.J. Nederhof: Bibliometric monitoring of research performance in the social sciences and the humanities: A review, Scientometrics 66(1), 81–100 (2006)

    Article  Google Scholar 

  56. M. Huang, Y. Chang: Characteristics of research output in social sciences and humanities: From a research evaluation perspective, J. Am. Soc. Inf. Sci. Technol. 59(11), 1819–1828 (2008)

    Article  Google Scholar 

  57. G. Sivertsen, B. Larsen: Comprehensive bibliographic coverage of the social sciences and humanities in a citation index: An empirical analysis of the potential, Scientometrics 91(2), 567–575 (2012)

    Article  Google Scholar 

  58. T.N. Van Leeuwen, H.F. Moed, R.J.W. Tijssen, M.S. Visser, A.F.J. Van Raan: Language biases in the coverage of the Science Citation Index and its consequences for international comparisons of national research performance, Scientometrics 51(1), 335–346 (2001)

    Article  Google Scholar 

  59. M. Zitt, S. Ramanana-Rahary, E. Bassecoulard: Correcting glasses help fair comparisons in international science landscape: Country indicators as a function of ISI database delineation, Scientometrics 56(2), 259–282 (2003)

    Article  Google Scholar 

  60. V. Larivière, É. Archambault, Y. Gingras, É. Vignola-Gagné: The place of serials in referencing practices: Comparing natural sciences and engineering with social sciences and humanities, J. Am. Soc. Inf. Sci. Technol. 57(8), 997–1004 (2006)

    Article  Google Scholar 

  61. C. Michels, U. Schmoch: The growth of science and database coverage, Scientometrics 93(3), 831–846 (2012)

    Article  Google Scholar 

  62. S. Mikki: Comparing Google Scholar and ISI Web of Science for earth sciences, Scientometrics 82(2), 321–331 (2010)

    Article  Google Scholar 

  63. A. Sinha, Z.Y.S. Shen, H. Ma, D. Eide, B.J.P. Hsu, K. Wang: An overview of Microsoft Academic Service (MAS) and applications. In: Proc. 24th Int. Conf. World Wide Web, Florence, Italy 2015, ed. by A. Gangemi, S. Leonardi, A. Panconesi (ACM, New York 2015) pp. 243–246

    Google Scholar 

  64. D. Herrmannova, P. Knoth: An analysis of the Microsoft Academic Graph, D-Lib Mag. (2016), https://doi.org/10.1045/september2016-herrmannova

    Article  Google Scholar 

  65. A.-W. Harzing, S. Alakangas: Microsoft academic: Is the phoenix getting wings?, Scientometrics 110(1), 371–383 (2017)

    Google Scholar 

  66. J.E. Gray, M.C. Hamilton, A. Hauser, M.M. Janz, J.P. Peters, F. Taggert: Scholarish: Google Scholar and its value to the sciences, Issues Sci. Technol. Librarianship (2012), https://doi.org/10.5062/F4MK69T9

    Article  Google Scholar 

  67. C. Labbé: Ike Antkare, one of the great stars in the scientific firmament, ISSI Newsletter 6(2), 48–52 (2010)

    Google Scholar 

  68. P. Jacsó: Metadata mega mess in Google Scholar, Online Inf. Rev. 34(1), 175–191 (2010)

    Article  Google Scholar 

  69. A.-W. Harzing, S. Alakangas: Google Scholar, Scopus and the Web of Science: A longitudinal and cross-disciplinary comparison, Scientometrics 106(2), 787–804 (2016)

    Article  Google Scholar 

  70. Q. Wang, L. Waltman: Large-scale analysis of the accuracy of the journal classification systems of Web of Science and Scopus, J. Informetrics 10(2), 347–364 (2016)

    Article  Google Scholar 

  71. M. Thelwall, S. Haustein, V. Larivière, C.R. Sugimoto: Do altmetrics work? Twitter and ten other social web services, PLoS ONE 8(5), e64841 (2013)

    Article  Google Scholar 

  72. S. Haustein, I. Peters, J. Bar-Ilan, J. Priem, H. Shema, J. Terliesner: Coverage and adoption of altmetrics sources in the bibliometric community, Scientometrics 101(2), 1145–1163 (2014)

    Article  Google Scholar 

  73. E. Mohammadi, M. Thelwall: Mendeley readership altmetrics for the social sciences and humanities: Research evaluation and knowledge flows, J. Assoc. Inf. Sci. Technol. 65(8), 1627–1638 (2014)

    Article  Google Scholar 

  74. Z. Zahedi, R. Costas, P. Wouters: How well developed are altmetrics? A cross-disciplinary analysis of the presence of “alternative metrics” in scientific publications, Scientometrics 101(2), 1491–1513 (2014)

    Article  Google Scholar 

  75. C.L. González-Valiente, J. Pacheco-Mendoza, R. Arencibia-Jorge: A review of altmetrics as an emerging discipline for research evaluation, Learn. Publ. 29(4), 229–238 (2016)

    Article  Google Scholar 

  76. A.E. Williams: Altmetrics: An overview and evaluation, Online Inf. Rev. 41(3), 311–317 (2017)

    Article  Google Scholar 

  77. C. Daraio, W. Glänzel: Grand challenges in data integration–state of the art and future perspectives: An introduction, Scientometrics 108(1), 391–400 (2016)

    Article  Google Scholar 

  78. OECD: Revised Field of Science and Technology (FOS) Classification in the Frascati Manual—Report number DSTI/EAS/STP/NESTI(2006)19/FINAL (OECD, Paris 2007)

    Google Scholar 

  79. E. Garfield: The evolution of the Science Citation Index, Int. Microbiol. 10(1), 65–69 (2007)

    Google Scholar 

  80. A.I. Pudovkin, E. Garfield: Algorithmic procedure for finding semantically related journals, J. Am. Soc. Inf. Sci. Technol. 53(13), 1113–1119 (2002)

    Article  Google Scholar 

  81. E. Garfield: Citation analysis as a tool in journal evaluation: Journals can be ranked by frequency and impact of citations for science policy studies, Science 178(4060), 471–479 (1972)

    Article  Google Scholar 

  82. E. Garfield: The history and meaning of the journal impact factor, J. Am. Med. Assoc. 295(1), 90–93 (2006)

    Article  Google Scholar 

  83. F. Narin, G. Pinski, H.H. Gee: Structure of the biomedical literature, J. Am. Soc. Inf. Sci. 27(1), 25–45 (1976)

    Article  Google Scholar 

  84. P. Jacsó: As we may search: Comparison of major features of the Web of Science, Scopus, and Google Scholar citation-based and citation-enhanced databases, Curr. Sci. 89(9), 1537–1547 (2005)

    Google Scholar 

  85. F. de Moya-Anegón, Z. Chinchilla-Rodríguez, B. Vargas-Quesada, E. Corera-Álvarez, F.J. Muñoz-Fernández, A. González-Molina, V. Herrero-Solana: Coverage analysis of Scopus: A journal metric approach, Scientometrics 73(1), 53–78 (2007)

    Article  Google Scholar 

  86. L. Leydesdorff, S.E. Cozzens: The delineation of specialties in terms of journals using the dynamic journal set of the SCI, Scientometrics 26(1), 135–156 (1993)

    Article  Google Scholar 

  87. E. Bassecoulard, M. Zitt: Indicators in a research institute: A multi-level classification of scientific journals, Scientometrics 44(3), 323–345 (1999)

    Article  Google Scholar 

  88. I. Rafols, M. Meyer: Diversity and network coherence as indicators of interdisciplinarity: Case studies in bionanoscience, Scientometrics 82(2), 263–287 (2009)

    Article  Google Scholar 

  89. W. Glänzel, A. Schubert: A new classification scheme of science fields and subfields designed for scientometric evaluation purposes, Scientometrics 56(3), 357–367 (2003)

    Article  Google Scholar 

  90. E. Archambault, O.H. Beauchesne, J. Caruso: Towards a multilingual, comprehensive and open scientific journal ontology. In: ISSI'11: Proc. 13th Int. Conf. Int. Soc. Scientometr. Informetrics, Durban, South Africa 2011, ed. by E. Noyons, P. Ngulube, J. Leta (ISSI, Leiden Univ. Zululand 2011) pp. 66–77

    Google Scholar 

  91. K.W. Boyack, R. Klavans: Creation of a highly detailed, dynamic, global model and map of science, J. Assoc. Inf. Sci. Technol. 65(4), 670–685 (2014)

    Article  Google Scholar 

  92. R. Klavans, K.W. Boyack: Which type of citation analysis generates the most accurate taxonomy of scientific and technical knowledge?, J. Assoc. Inf. Sci. Technol. 68(4), 984–998 (2017)

    Article  Google Scholar 

  93. A. Ruiz-Iniesta, O. Corcho: A review of ontologies for describing scholarly and scientific documents. In: SePublica'14: Proc. 4th Workshop on Semantic Publishing Co-Located with the 11th Extended Semantic Web Conference, Anissaras, Greece 2014 (CEUR-WS, Aachen 2014), http://ceur-ws.org/Vol.1155/paper-07.pdf

    Google Scholar 

  94. A.M. Petersen, D. Rotolo, L. Leydesdorff: A triple helix model of medical innovation: Supply, demand, and technological capabilities in terms of medical subject headings, Res. Policy 45(3), 666–681 (2016)

    Article  Google Scholar 

  95. A. Mogoutov, B. Kahane: Data search strategy for science and technology emergence: A scalable and evolutionary query for nanotechnology tracking, Res. Policy 36(6), 893–903 (2007)

    Article  Google Scholar 

  96. A.L. Porter, J. Youtie, P. Shapira, D.J. Schoeneck: Refining search terms for nanotechnology, J. Nanoparticle Res. 10(5), 715–728 (2007)

    Article  Google Scholar 

  97. P. Ingwersen: Cognitive perspectives of information retrieval interaction: Elements of a cognitive IR theory, J. Documentation 52(1), 3–50 (1996)

    Article  Google Scholar 

  98. J. Nicolaisen, B. Hjørland: Practical potentials of Bradford's law: A critical examination of the received view, J. Documentation 63(3), 359–377 (2007)

    Article  Google Scholar 

  99. P. Ingwersen, K. Järvelin: The Turn: Integration of Information Seeking and Retrieval in Context, The Information Retrieval Series, Vol. 18 (Springer, Dordrecht 2005)

    Google Scholar 

  100. T.E. Nisonger: Journals in the core collection: Definition, identification, and applications, Ser. Libr. 51(3/4), 51–73 (2007)

    Article  Google Scholar 

  101. H. Small: Co-citation in the scientific literature: A new measure of the relationship between two documents, J. Am. Soc. Inf. Sci. 24(4), 265–269 (1973)

    Article  Google Scholar 

  102. Q.L. Burrell: On the \(h\)-index, the size of the Hirsch core and Jin’s a-index, J. Informetrics 1(2), 170–177 (2007)

    Article  Google Scholar 

  103. W. Glänzel, B. Thijs: Using “core documents” for detecting and labelling new emerging topics, Scientometrics 91(2), 399–416 (2012)

    Article  Google Scholar 

  104. J. Rocchio: Relevance feedback in information retrieval. In: The SMART Retrieval System: Experiments in Automatic Document Processing, ed. by G. Salton (Prentice Hall, Englewood Cliffs 1971) pp. 313–323

    Google Scholar 

  105. G. Salton, C. Buckley: Improving retrieval performance by relevance feedback, J. Am. Soc. Inf. Sci. 41(4), 288–297 (1990)

    Article  Google Scholar 

  106. C. Carpineto, G. Romano: A survey of automatic query expansion in information retrieval, ACM Comput. Surv. 44(1), 1–50 (2012)

    Article  Google Scholar 

  107. R. Agrawal, T. Imieliński, A. Swami: Mining association rules between sets of items in large databases, ACM SIGMOD Rec. 22(2), 207–216 (1993)

    Article  Google Scholar 

  108. D. Hric, R.K. Darst, S. Fortunato: Community detection in networks: Structural communities versus ground truth, Phys. Rev. E 90(6), 062805 (2014)

    Google Scholar 

  109. M.M. Kessler: Bibliographic coupling between scientific papers, Am. Doc. 14(1), 10–25 (1963)

    Article  Google Scholar 

  110. N. Jardine, C.J. van Rijsbergen: The use of hierarchic clustering in information retrieval, Inf. Storage Retr. 7(5), 217–240 (1971)

    Article  Google Scholar 

  111. P. Mayr, A. Scharnhorst: Combining bibliometrics and information retrieval: Preface, Scientometrics 102(3), 2191–2192 (2015)

    Article  Google Scholar 

  112. P. Mayr, A. Scharnhorst: Scientometrics and information retrieval: Weak-links revitalized, Scientometrics 102(3), 2193–2199 (2015)

    Article  Google Scholar 

  113. M. Zitt: Meso-level retrieval: IR-bibliometrics interplay and hybrid citation-words methods in scientific fields delineation, Scientometrics 102(3), 2223–2245 (2015)

    Article  Google Scholar 

  114. P. Mayr, I. Frommholz, G. Cabanac, M.K. Chandrasekaran, K. Jaidka, M.-Y. Kan, D. Wolfram: Introduction to the special issue on bibliometric-enhanced information retrieval and natural language processing for digital libraries (BIRNDL), Int. J. Digit. Libr. 19(2-3), 107–111 (2018)

    Article  Google Scholar 

  115. M.E.J. Newman: The structure of scientific collaboration networks, Proc. Nat. Acad. Sci. 98(2), 404–409 (2001)

    Article  Google Scholar 

  116. M.E.J. Newman: Coauthorship networks and patterns of scientific collaboration, Proc. Nat. Acad. Sci. 101(Suppl. 1), 5200–5205 (2004)

    Article  Google Scholar 

  117. A.-L. Barabási, H. Jeong, Z. Néda, E. Ravasz, A. Schubert, T. Vicsek: Evolution of the social network of scientific collaborations, Physica A: Stat. Mech. Appl. 311(3/4), 590–614 (2002)

    Article  Google Scholar 

  118. D.J.D. Price: A general theory of bibliometric and other cumulative advantage processes, J. Am. Soc. Inf. Sci. 27(5), 292–306 (1976)

    Google Scholar 

  119. R. Albert, A.-L. Barabási: Statistical mechanics of complex networks, Rev. Mod. Phys. 74(1), 47–97 (2002)

    Article  Google Scholar 

  120. C.S. Wagner, L. Leydesdorff: Network structure, self-organization, and the growth of international collaboration in science, Res. Policy 34(10), 1608–1618 (2005)

    Article  Google Scholar 

  121. G. Csányi, B. Szendrői: Fractal–small-world dichotomy in real-world networks, Phys. Rev. E 70(1), 016122 (2004)

    Google Scholar 

  122. M. McPherson, L. Smith-Lovin, J.M. Cook: Birds of a feather: Homophily in social networks, Annu. Rev. Sociol. 27(1), 415–444 (2001)

    Article  Google Scholar 

  123. N. Carayol, P. Roux: Knowledge flows and the geography of networks: A strategic model of small world formation, J. Econ. Behav. Organ. 71(2), 414–427 (2009)

    Article  Google Scholar 

  124. K. Börner, W. Glänzel, A. Scharnhorst, P. van den Besselaar: Modeling science: Studying the structure and dynamics of science, Scientometrics 89(1), 347–348 (2011)

    Article  Google Scholar 

  125. M. Cadot, A. Lelu, M. Zitt: Benchmarking 17 clustering methods, https://hal.archives-ouvertes.fr/hal-01532894 (2018)

  126. A. McCallum, K. Nigam, L.H. Ungar: Efficient clustering of high-dimensional data sets with application to reference matching. In: KDD'00: Proc. 6th ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining, Boston, MA 2000, ed. by R. Ramakrishnan, S. Stolfo, R. Bayardo, I. Parsa (Association for Computing Machinery, New York 2000) pp. 169–178

    Chapter  Google Scholar 

  127. M. Zitt, E. Bassecoulard: Reassessment of co-citation methods for science indicators: Effect of methods improving recall rates, Scientometrics 37(2), 223–244 (1996)

    Article  Google Scholar 

  128. K.W. Boyack, R. Klavans: Co-citation analysis, bibliographic coupling, and direct citation: Which citation approach represents the research front most accurately?, J. Am. Soc. Inf. Sci. Technol. 61(12), 2389–2404 (2010)

    Article  Google Scholar 

  129. G.W. Milligan: A review of Monte Carlo tests of cluster analysis, Multivar. Behav. Res. 16(3), 379–407 (1981)

    Article  Google Scholar 

  130. G.W. Milligan, M.C. Cooper: Methodology review: Clustering methods, Appl. Psychol. Meas. 11(4), 329–354 (1987)

    Article  Google Scholar 

  131. M. Ester, H.-P. Kriegel, J. Sander, X. Xu: A density-based algorithm for discovering clusters a density-based algorithm for discovering clusters in large spatial databases with noise. In: KDD'96: Proc. 2nd Int. Con. Knowl. Discovery Data Mining, Portland, OR 1996, ed. by E. Simoudis, J. Han, U. Fayyad (AAAI, Palo Alto 1996) pp. 226–231

    Google Scholar 

  132. A. Rodriguez, A. Laio: Clustering by fast search and find of density peaks, Science 344(6191), 1492–1496 (2014)

    Article  Google Scholar 

  133. M. Reinert: Un logiciel d'analyse lexicale, Cah. Ana. Données 11(4), 471–481 (1986)

    Google Scholar 

  134. J.-P. Benzécri: L'analyse des Correspondances, Analyse des Données, Vol. 2 (Dunod, Paris 1973)

    Google Scholar 

  135. P.D. Turney, P. Pantel: From frequency to meaning: Vector space models of semantics, J. Artif. Intell. Res. 37(1), 141–188 (2010)

    Article  Google Scholar 

  136. S. Deerwester, S.T. Dumais, T.K. Landauer, G.W. Furnas, L. Beck: Improving information retrieval with latent semantic indexing. In: Proc. 51st Annu. Meet. Am. Soc. Inf. Sci., Atlanta (1988) pp. 36–40

    Google Scholar 

  137. A. Lelu: Clusters and factors: Neural algorithms for a novel representation of huge and highly multidimensional data sets. In: New Approaches in Classification and Data Analysis, ed. by E. Diday, Y. Lechevallier, M. Schader, P. Bertrand (Springer, Berlin 1994) pp. 241–248

    Chapter  Google Scholar 

  138. C.H. Papadimitriou, G. Tamaki, P. Raghavan, S. Vempala: Latent semantic indexing: A probabilistic analysis. In: PODS'98: Proc. 17th ACM SIGACT-SIGMOD-SIGART Symp. Principles Database Syst., Seattle, WA 1998, ed. by A. Mendelson, J. Paredaens (ACM, New York 1998) pp. 159–168

    Chapter  Google Scholar 

  139. T. Hofmann: Probabilistic latent semantic indexing. In: SIGIR'99: Proc. 22nd Annu. Int. ACM SIGIR Conf. Res. Dev. Inf, Retrieval, Berkeley, CA 1999, ed. by F. Gey, M. Hearst, R. Tong (ACM, New York 1999) pp. 50–57

    Chapter  Google Scholar 

  140. D.M. Blei, A.Y. Ng, M.I. Jordan: Latent Dirichlet allocation, J. Mach. Learning Res. 3, 993–1022 (2003)

    Google Scholar 

  141. V.D. Blondel, J.-L. Guillaume, R. Lambiotte, E. Lefebvre: Fast unfolding of communities in large networks, J. Stat. Mech. Theory Exp. 2008(10), P10008 (2008)

    Article  Google Scholar 

  142. M. Rosvall, C.T. Bergstrom: An information-theoretic framework for resolving community structure in complex networks, Proc. Natl. Acad. Sci. 104(18), 7327–7331 (2007)

    Article  Google Scholar 

  143. N.J. van Eck, L. Waltman: Software survey: VOSviewer, a computer program for bibliometric mapping, Scientometrics 84(2), 523–538 (2010)

    Article  Google Scholar 

  144. M. Meila, J. Shi: Learning segmentation by random walks. In: NIPS'00: Proc. Neural Inf. Process. Syst. Conf., Denver, CO 2000, ed. by T.K. Leen, T.G. Dietterich, V. Tresp (MIT Press, Cambridge 2000) pp. 873–879

    Google Scholar 

  145. A. Lancichinetti, S. Fortunato: Community detection algorithms: A comparative analysis, Phys. Rev. E 80(5), 056117 (2009)

    Google Scholar 

  146. J. Leskovec, K.J. Lang, M. Mahoney: Empirical comparison of algorithms for network community detection. In: WWW'10: Proc. 19th Int. Conf. World Wide Web, Raleigh, NC 2010, ed. by M. Rappa, P. Jones, J. Freire, S. Chakrabarti (ACM, New York 2010) pp. 631–640

    Chapter  Google Scholar 

  147. J. Yang, J. Leskovec: Defining and evaluating network communities based on ground-truth. In: ICDM'12: Proc. 12th Int. Conf. Data Mining, Brussels 2012, ed. by M.J. Zaki, A. Siebes, J.X. Yu, B. Goethals, G. Webb, X. Wu (IEEE, Los Alamitos 2012) pp. 745–754

    Google Scholar 

  148. Y. Shen, X. He, J. Gao, L. Deng, G. Mesnil: A latent semantic model with convolutional-pooling structure for information retrieval. In: CIKM'14: Proc. 23rd ACM Conf. Inf. Knowl. Mining, Shanghai 2014, ed. by J. Li, X.S. Wang, M. Garofalakis, I. Soboroff, T. Suel, M. Wang (ACM, New York 2014) pp. 101–110

    Chapter  Google Scholar 

  149. C. Van Gysel, M. de Rijke, E. Kanoulas: Neural vector spaces for unsupervised information retrieval, ACM Trans. Inf. Syst. 36(4), 1–25 (2018)

    Article  Google Scholar 

  150. T. Mikolov, W. tau Yih, G. Zweig: Linguistic regularities in continuous space word representations. In: NAACL-HLT'13: Proc. Conf. North Am. Chap. Assoc. Comput. Linguistics: Human Lang. Technol., Atlanta, GA 2013, ed. by L. Vanderwende, H. Daume III, K. Kirchhoff (Association for Computational Linguistics, Stroudsburg 2013) pp. 746–751

    Google Scholar 

  151. O. Levy, Y. Goldberg: Neural word embedding as implicit matrix factorization. In: NIPS'14: Proc. Neural Inf. Process. Syst. Conf., Monreéal 2014, ed. by Z. Ghahramani, M. Welling, C. Cortes, N. Lawrence (Curran Associates, Red Hook 2014) pp. 2177–2185

    Google Scholar 

  152. S.E. Robertson, S. Walker, S. Jones, M. Hancock-Beaulieu, M. Gatford: Okapi at TREC-3. In: TREC'94: Proc. 3rd Text Retrieval Conf., Gaithersburg, MA 1994, ed. by D.K. Harman (NIST, Gaithersburg 1994) pp. 109–126

    Google Scholar 

  153. T.M.J. Fruchterman, E.M. Reingold: Graph drawing by force-directed placement, Softw. Pract. Exp. 21(11), 1129–1164 (1991)

    Article  Google Scholar 

  154. M. Bastian, S. Heymann, M. Jacomy: Gephi: An open source software for exploring and manipulating networks. In: ICWSM'09: Proc. 3rd Int. AAAI Conf. Weblogs Soc. Media, San Jose, CA 2009, ed. by W.W. Cohen, N. Nicolov (AAAI, Palo Alto 2009) p. 361

    Google Scholar 

  155. S. Martin, W.M. Brown, R. Klavans, K.W. Boyack: OpenOrd: An open-source toolbox for large graph layout. In: Proc. Visualization Data Analysis 2011, San Francisco, CA 2011, ed. by P.C. Wong, J. Park, M.C. Hao, C. Chen, K. Börner, D.L. Kao, J.C. Roberts (SPIE, Bellingham 2011) p. 786806

    Google Scholar 

  156. W. de Nooy, A. Mrvar, V. Batagelj: Exploratory Social Network Analysis with Pajek, Revised and Expanded, 2nd edn. (Cambridge Univ. Press, New York 2011)

    Book  Google Scholar 

  157. M. Cadot, A. Lelu: Optimized representation for classifying qualitative data. In: DBKDA'10: Proc. 2nd Int. Conf. Adv. Databases, Knowl., Data Applications, Les Menuires, France 2010, ed. by F. Laux, L. Strömbäck (IEEE, Los Alamitos 2010) pp. 241–246

    Chapter  Google Scholar 

  158. D. Cai, X. He, J. Han: Document clustering using locality preserving indexing, IEEE Trans. Knowl. Data Eng. 17(12), 1624–1637 (2005)

    Article  Google Scholar 

  159. W.M. Rand: Objective criteria for the evaluation of clustering methods, J. Am. Stat. Assoc. 66(336), 846–850 (1971)

    Article  Google Scholar 

  160. T.M. Cover, J.A. Thomas: Elements of Information Theory, Wiley Series in Telecommunications (Wiley, New York 1991)

    Book  Google Scholar 

  161. P. Ronhovde, Z. Nussinov: Multiresolution community detection for megascale networks by information-based replica correlations, Phys. Rev. E 80(1), 016109 (2009)

    Google Scholar 

  162. E. Garfield, A.I. Pudovkin, V.S. Istomin: Why do we need algorithmic historiography?, J. Am. Soc. Inf. Sci. Technol. 54(5), 400–412 (2003)

    Article  Google Scholar 

  163. I. Marshakova: System of document connections based on references, Nauchno-Tekh. Inf. 2 6, 3–8 (1973)

    Google Scholar 

  164. H.D. White, B.C. Griffith: Author cocitation: A literature measure of intellectual structure, J. Am. Soc. Inf. Sci. 32(3), 163–171 (1981)

    Article  Google Scholar 

  165. G. Salton: The SMART Retrieval System: Experiments in Automatic Document Processing (Prentice Hall, Englewood Cliffs 1971)

    Google Scholar 

  166. M. Callon, J.-P. Courtial, W.A. Turner, S. Bauin: From translations to problematic networks: An introduction to co-word analysis, Soc. Sci. Inf. 22(2), 191–235 (1983)

    Article  Google Scholar 

  167. W.A. Turner, G. Chartron, F. Laville, B. Michelet: Packaging information for peer review: New co-word analysis techniques. In: Handbook of Quantitative Science and Technology, ed. by A.F.J. van Raan (Springer, Dordrecht 1988) pp. 291–323

    Chapter  Google Scholar 

  168. J. Whittaker: Creativity and conformity in science: Titles, keywords and co-word analysis, Soc. Stud. Sci. 19(3), 473–496 (1989)

    Article  Google Scholar 

  169. L.C. Freeman: The Development of Social Network Analysis: A Study in the Sociology of Science (Empirical, Vancouver 2004)

    Google Scholar 

  170. C. Chen: CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature, J. Am. Soc. Inf. Sci. Technol. 57(3), 359–377 (2006)

    Article  Google Scholar 

  171. W. Glänzel, H.-J. Czerwon: A new methodological approach to bibliographic coupling and its application to the national, regional and institutional level, Scientometrics 37(2), 195–221 (1996)

    Article  Google Scholar 

  172. L. Waltman, N.J. van Eck: A new methodology for constructing a publication-level classification system of science, J. Am. Soc. Inf. Sci. Technol. 63(12), 2378–2392 (2012)

    Article  Google Scholar 

  173. N. Shibata, Y. Kajikawa, Y. Takeda, K. Matsushima: Comparative study on methods of detecting research fronts using different types of citation, J. Am. Soc. Inf. Sci. Technol. 60(3), 571–580 (2009)

    Article  Google Scholar 

  174. B. Jarneving: A comparison of two bibliometric methods for mapping of the research front, Scientometrics 65(2), 245–263 (2005)

    Article  Google Scholar 

  175. K. Börner: Atlas of Science: Visualizing What We Know (MIT Press, Cambridge 2010)

    Google Scholar 

  176. M. Zitt, E. Bassecoulard: Development of a method for detection and trend analysis of research fronts built by lexical or cocitation analysis, Scientometrics 30(1), 333–351 (1994)

    Article  Google Scholar 

  177. L. Leydesdorff, I. Rafols: Interactive overlays: A new method for generating global journal maps from web-of-science data, J. Informetrics 6(2), 318–332 (2012)

    Article  Google Scholar 

  178. L. Leydesdorff, P. Zhou: Nanotechnology as a field of science: Its delineation in terms of journals and patents, Scientometrics 70(3), 693–713 (2007)

    Article  Google Scholar 

  179. K.W. Boyack: Investigating the effect of global data on topic detection, Scientometrics 111(2), 999–1015 (2017)

    Article  Google Scholar 

  180. C. Bergstrom: Eigenfactor: Measuring the value and prestige of scholarly journals, College Res. Libr. News 68(5), 314–316 (2007)

    Article  Google Scholar 

  181. M. Zitt, H. Small: Modifying the journal impact factor by fractional citation weighting: The audience factor, J. Am. Soc. Inf. Sci. Technol. 59(11), 1856–1860 (2008)

    Article  Google Scholar 

  182. L. Waltman, N.J. van Eck, T.N. van Leeuwen, M.S. Visser: Some modifications to the SNIP journal impact indicator, J. Informatrics 7(2), 272–285 (2013)

    Article  Google Scholar 

  183. M. Zitt, J.-P. Cointet: Citation impacts revisited: How novel impact measures reflect interdisciplinarity and structural change at the local and global level. In: ISSI'13: Proc. 14th Int. Conf. Int. Soc. Scientometr. Informetrics, Vienna, Austria 2013, ed. by J. Gorraiz, E. Schiebel (Austrian Institute of Technology, Vienna 2013) pp. 285–299

    Google Scholar 

  184. H. Small, E. Sweeney: Clustering the Science Citation Index® using co-citations: I. A comparison of methods, Scientometrics 7(3–6), 391–409 (1985)

    Article  Google Scholar 

  185. T. Luukkonen, R.J.W. Tijssen, O. Persson, G. Sivertsen: The measurement of international scientific collaboration, Scientometrics 28(1), 15–36 (1993)

    Article  Google Scholar 

  186. M. Zitt, E. Bassecoulard, Y. Okubo: Shadows of the past in international cooperation: Collaboration profiles of the top five producers of science, Scientometrics 47(3), 627–657 (2000)

    Article  Google Scholar 

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

    Article  Google Scholar 

  188. G. Lewison, G. Paraje: The classification of biomedical journals by research level, Scientometrics 60(2), 145–157 (2004)

    Article  Google Scholar 

  189. S. Teufel, J. Carletta, M. Moens: An annotation scheme for discourse-level argumentation in research articles. In: EACL'99: Proc. 9th Conf. Eur. Chap. Assoc. Comput. Linguistics, Bergen, Norway 1999, ed. by H.S. Thompson, A. Lascarides (ACL, Stroudsburg 1999) pp. 110–117

    Google Scholar 

  190. M. Liakata, S. Saha, S. Dobnik, C. Batchelor, D. Rebholz-Schuhmann: Automatic recognition of conceptualization zones in scientific articles and two life science applications, Bioinformatics 28(7), 991–1000 (2012)

    Article  Google Scholar 

  191. S. Teufel, M. Moens: Summarizing scientific articles: Experiments with relevance and rhetorical status, Comput. Linguist. 28(4), 409–445 (2002)

    Article  Google Scholar 

  192. C. Lyon, J. Malcolm, B. Dickerson: Detecting short passages of similar text in large document collections. In: EMNLP'01: Proc. Conf. Empirical Methods Natur. Lang. Process., Pittsburgh, PA 2001, ed. by L. Lee, D. Harman (ACL, Stroudsburg 2001) pp. 118–125

    Google Scholar 

  193. R. Cilibrasi, P.M.B. Vitányi: Clustering by compression, IEEE Trans. Inf. Theory 51(4), 1523–1545 (2005)

    Article  Google Scholar 

  194. C.H. Bennett, P. Gács, M. Li, P.M.B. Vitányi, W.H. Zurek: Information distance, IEEE Trans. Inf. Theory 44(4), 1407–1423 (1998)

    Article  Google Scholar 

  195. M. Li, X. Chen, X. Li, B. Ma, P.M.B. Vitányi: The similarity metric, IEEE Trans. Inf. Theory 50(12), 3250–3264 (2004)

    Article  Google Scholar 

  196. R. Cilibrasi, P.M.B. Vitányi: The Google similarity distance, IEEE Trans. Knowl. Data Eng. 19(3), 370–383 (2007)

    Article  Google Scholar 

  197. J. MacQueen: Some methods for classification and analysis of multivariate observations. In: Proc. 5th Berkeley Symp. Math. Statistics Probabilities, Durban, South Africa 1967, ed. by L.M. Le Cam, J. Neyman (Univ. California, Berkeley 1967) pp. 281–297

    Google Scholar 

  198. E.W. Forgy: Cluster analysis of multivariate data: Efficiency versus interpretability of classifications, Biometrics 21, 768–769 (1965)

    Google Scholar 

  199. N. Sahoo, J. Callan, R. Krishnan, G. Duncan, R. Padman: Incremental hierarchical clustering of text documents. In: CIKM'06: Proc. 15th ACM Int. Conf. Inf. Knowl. Manag., Arlington, VA 2006, ed. by P.S. Yu, V. Tsotras, E. Fox, B. Liu (ACM, New York 2006) pp. 357–366

    Chapter  Google Scholar 

  200. H. Yu, D. Searsmith, X. Li, J. Han: Scalable construction of topic directory with nonparametric closed termset mining. In: ICDM'04: Proc. 4th IEEE Int. Conf. Data Mining, Brighton 2004, ed. by R. Rastogi, K. Morik, M. Bramer, X. Wu (IEEE, Los Alamitos 2004) pp. 1–4

    Google Scholar 

  201. F. Åström: Changes in the LIS research front: Time-sliced cocitation analyses of LIS journal articles, 1990–2004, J. Am. Soc. Inf. Sci. Technol. 58(7), 947–957 (2007)

    Article  Google Scholar 

  202. D.M. Blei, J.D. Lafferty: Dynamic topic models. In: ICML'06: Proc. 23rd Int. Conf. Mach. Learning, Pittsburgh, PA 2006, ed. by W.W. Cohen, A. Moore (ACM, New York 2006) pp. 113–120

    Chapter  Google Scholar 

  203. Q. Mei, C.X. Zhai: Discovering evolutionary theme patterns from text: An exploration of temporal text mining. In: KDD'05: Proc. 11th ACM SIGKDD Int. Conf. Knowl. Discov. Data Mining, Chicago, IL 2005, ed. by R.L. Grossman, R. Bayardo, K. Bennett, J. Vaidya (Association for Computing Machinery, New York 2005) pp. 198–207

    Chapter  Google Scholar 

  204. F. Janssens, W. Glänzel, B. De Moor: Dynamic hybrid clustering of bioinformatics by incorporating text mining and citation analysis. In: KDD'07: Proc. 13th ACM SIGKDD Int. Conf. Knowl. Discov. Data Mining, San Jose, CA 2007, ed. by P. Berkhin, R. Caruana, X. Wu, S. Gaffney (Association for Computing Machinery, New York 2007) pp. 360–369

    Chapter  Google Scholar 

  205. C. Chen, F. Ibekwe-SanJuan, J. Hou: The structure and dynamics of cocitation clusters: A multiple-perspective cocitation analysis, J. Am. Soc. Inf. Sci. Technol. 61(7), 1386–1409 (2010)

    Article  Google Scholar 

  206. D. Chavalarias, J.-P. Cointet: Phylomemetic patterns in science evolution—the rise and fall of scientific fields, PLOS ONE 8(2), e54847 (2013)

    Article  Google Scholar 

  207. S. Shane: Technological Opportunities and New Firm Creation, Manag. Sci. 47(2), 205–220 (2001)

    Article  Google Scholar 

  208. K.B. Dahlin, D.M. Behrens: When is an invention really radical? Defining and measuring technological radicalness, Res. Policy 34(5), 717–737 (2005)

    Article  Google Scholar 

  209. H. Small, H. Tseng, M. Patek: Discovering discoveries: Identifying biomedical discoveries using citation contexts, J. Informetrics 11(1), 46–62 (2017)

    Article  Google Scholar 

  210. E. Garfield, I.H. Sher: KeyWords Plus™—algorithmic derivative indexing, J. Am. Soc. Inf. Sci. 44(5), 298–299 (1993)

    Article  Google Scholar 

  211. R.N. Kostoff, J.A. del Río, J.A. Humenik, E.O. García, A.M. Ramírez: Citation mining: Integrating text mining and bibliometrics for research user profiling, J. Am. Soc. Inf. Sci. Technol. 52(13), 1148–1156 (2001)

    Article  Google Scholar 

  212. B. Verspagen, C. Werker: The invisible college of the economics of innovation and technological change, Estudios de Economía Aplicada 21(3), 187–203 (1975)

    Google Scholar 

  213. D. D. Beaver, R. Rosen: Studies in scientific collaboration – Part I. The professional origins of scientific co-authorship, Scientometrics 1(3), 231–245 (1979)

    Article  Google Scholar 

  214. T. Luukkonen, O. Persson, G. Sivertsen: Understanding patterns of international scientific collaboration, Sci. Technol. Hum. Values 17(1), 101–126 (1992)

    Article  Google Scholar 

  215. H. Kretschmer: Coauthorship networks of invisible colleges and institutionalized communities, Scientometrics 30(1), 363–369 (1994)

    Article  Google Scholar 

  216. J.S. Katz, B.R. Martin: What is research collaboration?, Res. Policy 26(1), 1–18 (1997)

    Article  Google Scholar 

  217. J.S. Katz: Geographical proximity and scientific collaboration, Scientometrics 31(1), 31–43 (1994)

    Article  Google Scholar 

  218. J. Hoekman, K. Frenken, R.J.W. Tijssen: Research collaboration at a distance: Changing spatial patterns of scientific collaboration within Europe, Res. Policy 39(5), 662–673 (2010)

    Article  Google Scholar 

  219. T. Velden, A. Haque, C. Lagoze: A new approach to analyzing patterns of collaboration in co-authorship networks: Mesoscopic analysis and interpretation, Scientometrics 85(1), 219–242 (2010)

    Article  Google Scholar 

  220. P. Mutschke, A.Q. Haase: Collaboration and cognitive structures in social science research fields. Towards socio-cognitive analysis in information systems, Scientometrics 52(3), 487–502 (2001)

    Article  Google Scholar 

  221. J. Raffo, S. Lhuillery: How to play the “Names Game”: Patent retrieval comparing different heuristics, Res. Policy 38(10), 1617–1627 (2009)

    Article  Google Scholar 

  222. K.W. McCain: The author cocitation structure of macroeconomics, Scientometrics 5(5), 277–289 (1983)

    Article  Google Scholar 

  223. G.N. Gilbert: Referencing as Persuasion, Soc. Stud. Sci. 7(1), 113–122 (1977)

    Article  Google Scholar 

  224. C. Roth, J.-P. Cointet: Social and semantic coevolution in knowledge networks, Soc. Netw. 32(1), 16–29 (2010)

    Article  Google Scholar 

  225. X. Polanco, L. Grivel, J. Royauté: How to do things with terms in informetrics: Terminological variation and stabilization as science watch indicators. In: ISSI'95: Proc. 5th Int. Conf. Int. Soc. Scientometr. Informetrics, River Forest, IL 1995, ed. by M.E.D. Koenig, A. Bookstein (Learned Information, Medford 1995) pp. 435–444

    Google Scholar 

  226. M.F. Porter: An algorithm for suffix stripping, Program 14(3), 130–137 (1980)

    Article  Google Scholar 

  227. L. Egghe, R. Rousseau: Introduction to Informetrics: Quantitative Methods in Library, Documentation, and Information Science (Elsevier, Amsterdam 1990)

    Google Scholar 

  228. K.W. McCain: Descriptor and citation retrieval in the medical behavioral sciences literature: Retrieval overlaps and novelty distribution, J. Am. Soc. Inf. Sci. 40(2), 110–114 (1989)

    Article  Google Scholar 

  229. M.L. Pao: Term and citation retrieval: A field study, Inf. Process. Manag. 29(1), 95–112 (1993)

    Article  Google Scholar 

  230. L. Bornmann, H.-D. Daniel: What do citation counts measure? A review of studies on citing behavior, J. Documentation 64(1), 45–80 (2008)

    Article  Google Scholar 

  231. B. Cronin: The Citation Process: The Role Significance of Citations in Scientific Communication (Taylor Graham, London 1984)

    Google Scholar 

  232. H.G. Small: Cited documents as concept symbols, Soc. Stud. Sci. 8(3), 327–340 (1978)

    Article  Google Scholar 

  233. B. Latour: Science in Action: How to Follow Scientists and Engineers Through Society (Harvard Univ. Press, Cambridge 1987)

    Google Scholar 

  234. A. Cambrosio, P. Keating, S. Mercier, G. Lewison, A. Mogoutov: Mapping the emergence and development of translational cancer research, Eur. J. Cancer 42(18), 3140–3148 (2006)

    Article  Google Scholar 

  235. F. Narin, E. Noma: Is technology becoming science?, Scientometrics 7(3–6), 369–381 (1985)

    Article  Google Scholar 

  236. M. Callon: Pinpointing industrial invention: An exploration of quantitative methods for the analysis of patents. In: Mapping the Dynamics of Science and Technology, ed. by M. Callon, J. Law, A. Rip (Macmillan, Houndmills, London 1986) pp. 163–188

    Chapter  Google Scholar 

  237. V. Lariviére, É. Archambault, Y. Gingras: Long-term variations in the aging of scientific literature: From exponential growth to steady-state science (1900–2004), J. Am. Soc. Inf. Sci. Technol. 59(2), 288–296 (2008)

    Article  Google Scholar 

  238. E.C.M. Noyons, R.K. Buter, A.F.J. van Raan, H. Schwechheimer, M. Winterhager, P. Weingart: The Role of Europe in World-Wide Science and Technology: Monitoring and Evaluation in a Context of Global Competition—Report for the European Commission (CWTS-Leiden, IWT-Bielefeld, Brussels 2000)

    Google Scholar 

  239. E.C.M. Noyons, R.K. Buter, A.F.J. van Raan, U. Schmoch, T. Heinze, S. Hinze, R. Rangnow: Mapping Excellence in Science and Technology Across Europe Nanoscience and Nanotechnology—Report of Project EC-PPN CT-2002-0001 to the European Commission (CWTS and Fraunhofer ISI, Leiden, Karlsruhe 2003)

    Google Scholar 

  240. M. Zitt, A. Lelu, E. Bassecoulard: Hybrid citation-word representations in science mapping: Portolan charts of research fields?, J. Am. Soc. Inf. Sci. Technol. 62(1), 19–39 (2011)

    Article  Google Scholar 

  241. P. Laurens, M. Zitt, E. Bassecoulard: Delineation of the genomics field by hybrid citation-lexical methods: Interaction with experts and validation process, Scientometrics 82(3), 647–662 (2010)

    Article  Google Scholar 

  242. S.A. Morris, G.G. Yen: Crossmaps: Visualization of overlapping relationships in collections of journal papers, Proc. Natl. Acad. Sci. 101(Suppl. 1), 5291–5296 (2004)

    Article  Google Scholar 

  243. C. Reilly, C. Wang, M. Rutherford: A rapid method for the comparison of cluster analyses, Stat. Sin. 15(1), 19–33 (2005)

    Google Scholar 

  244. R. Klavans, K.W. Boyack: Toward a consensus map of science, J. Am. Soc. Inf. Sci. Technol. 60(3), 455–476 (2009)

    Article  Google Scholar 

  245. L. Leydesdorff, I. Rafols: A global map of science based on the ISI subject categories, J. Am. Soc. Inf. Sci. Technol. 60(2), 348–362 (2009)

    Article  Google Scholar 

  246. P. Ahlgren, B. Jarneving: Bibliographic coupling, common abstract stems and clustering: A comparison of two document-document similarity approaches in the context of science mapping, Scientometrics 76(2), 273–290 (2008)

    Article  Google Scholar 

  247. E. Yan, Y. Ding: Scholarly network similarities: How bibliographic coupling networks, citation networks, cocitation networks, topical networks, coauthorship networks, and coword networks relate to each other, J. Am. Soc. Inf. Sci. Technol. 63(7), 1313–1326 (2012)

    Article  Google Scholar 

  248. T. Velden, K.W. Boyack, J. Gläser, R. Koopman, A. Scharnhorst, S. Wang: Comparison of topic extraction approaches and their results, Scientometrics 111(2), 1169–1221 (2017)

    Article  Google Scholar 

  249. H. Small: Co-Citation Context Analyses and the Structure of Paradigms, J. Documentation 36(3), 183–196 (1980)

    Article  Google Scholar 

  250. H. Small: Maps of science as interdisciplinary discourse: Co-citation contexts and the role of analogy, Scientometrics 83(3), 835–849 (2010)

    Article  Google Scholar 

  251. S. Teufel, A. Siddharthan, D. Tidhar: Automatic classification of citation function. In: EMNLP'06: Proc. Conf. Empirical Methods Nat. Lang. Process., Sydney, Australia 2006, ed. by D. Jurafsky, É. Gaussier (ACL, Stroudsburg 2006) pp. 103–110

    Chapter  Google Scholar 

  252. A. Ritchie, S. Robertson, S. Teufel: Comparing citation contexts for information retrieval. In: CIKM'08: Proc. 17th ACM Conf. Inf. Knowl. Mining, Napa Valley, CA 2008, ed. by J.G. Shanahan, S. A.-Yahia, I. Manolescu, Y. Zhang, D.A. Evans, A. Kolcz, K.-S. Choi, A. Chowdury (Association for Computing Machinery, New York 2008) pp. 213–222

    Chapter  Google Scholar 

  253. S. Liu, C. Chen: The differences between latent topics in abstracts and citation contexts of citing papers, J. Am. Soc. Inf. Sci. Technol. 64(3), 627–639 (2013)

    Article  Google Scholar 

  254. H. Small: Interpreting maps of science using citation context sentiments: A preliminary investigation, Scientometrics 87(2), 373–388 (2011)

    Article  Google Scholar 

  255. A. Elkiss, S. Shen, A. Fader, G. Erkan, D. States, D. Radev: Blind men and elephants: What do citation summaries tell us about a research article?, J. Am. Soc. Inf. Sci. Technol. 59(1), 51–62 (2008)

    Article  Google Scholar 

  256. A. Callahan, S. Hockema, G. Eysenbach: Contextual cocitation: Augmenting cocitation analysis and its applications, J. Am. Soc. Inf. Sci. Technol. 61(6), 1130–1143 (2010)

    Google Scholar 

  257. X. He, C.H.Q. Ding, H. Zha, H.D. Simon: Automatic topic identification using webpage clustering. In: ICDM'01: Proc. Int. Conf. Data Mining, San Jose, CA 2001, ed. by N. Cercone, T.Y. Lin, X. Wu (IEEE, Los Alamitos 2001) pp. 195–202

    Google Scholar 

  258. S. Brin, L. Page: The anatomy of a large-scale hypertextual web search engine, Comp. Netw. ISDN Syst. 30(1–7), 107–117 (1998)

    Article  Google Scholar 

  259. P. van den Besselaar, G. Heimeriks: Mapping research topics using word-reference co-occurrences: A method and an exploratory case study, Scientometrics 68(3), 377–393 (2006)

    Article  Google Scholar 

  260. P. Ahlgren, C. Colliander: Document–document similarity approaches and science mapping: Experimental comparison of five approaches, J. Informetrics 3(1), 49–63 (2009)

    Article  Google Scholar 

  261. F. Janssens, W. Glänzel, B. De Moor: A hybrid mapping of information science, Scientometrics 75(3), 607–631 (2008)

    Article  Google Scholar 

  262. W. Glänzel, B. Thijs: Using “core documents” for the representation of clusters and topics, Scientometrics 88(1), 297–309 (2011)

    Article  Google Scholar 

  263. R. Koopman, S. Wang, A. Scharnhorst: Contextualization of topics: Browsing through the universe of bibliographic information, Scientometrics 111(2), 1119–1139 (2017)

    Article  Google Scholar 

  264. Y. LeCun: A path to AI. In: BAI'17: Workshop Beneficial Artif. Intell., Asilomar, CA 2017, ed. by E. Brynjolfsson, E. Horvitz, P. Norvig, F. Rossi, S. Russell, B. Selman (Future of Life Institute, Cambridge 2017), https://futureoflife.org/wp-content/uploads/2017/01/Yann-LeCun.pdf

    Google Scholar 

  265. R.R. Braam, H.F. Moed, A.F.J. van Raan: Mapping of science by combined co-citation and word analysis. I. Structural aspects, J. Am. Soc. Inf. Sci. 42(4), 233–251 (1991)

    Article  Google Scholar 

  266. B. Larsen: Exploiting citation overlaps for information retrieval: Generating a boomerang effect from the network of scientific papers, Scientometrics 54(2), 155–178 (2002)

    Article  Google Scholar 

  267. Y. Huang, J. Schuehle, A.L. Porter, J. Youtie: A systematic method to create search strategies for emerging technologies based on the Web of Science: Illustrated for “Big Data”, Scientometrics 105(3), 2005–2022 (2015)

    Article  Google Scholar 

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Zitt, M., Lelu, A., Cadot, M., Cabanac, G. (2019). Bibliometric Delineation of Scientific Fields. In: Glänzel, W., Moed, H.F., Schmoch, U., Thelwall, M. (eds) Springer Handbook of Science and Technology Indicators. Springer Handbooks. Springer, Cham. https://doi.org/10.1007/978-3-030-02511-3_2

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