Allison, P. D., & Stewart, J. A. (1974). Productivity differences among scientists: Evidence for accumulative advantage. American Sociological Review,
39(4), 596–606.
Article
Google Scholar
Ball, P. (2005). Index aims for fair ranking of scientists. Nature,
436(7053), 900.
Article
Google Scholar
Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. Journal of Machine Learning Research,
3, 993–1022.
MATH
Google Scholar
Blondel, V. D., Guillaume, J. L., Lambiotte, R., & Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment,
2008(10), P10008.
Article
Google Scholar
Boyack, K. W., Newman, D., Duhon, R. J., Klavans, R., Patek, M., Biberstine, J. R., et al. (2011). Clustering more than two million biomedical publications: Comparing the accuracies of nine text-based similarity approaches. PLoS ONE,
6(3), e18029.
Article
Google Scholar
Brown, C. M. (1999). Information seeking behavior of scientists in the electronic information age: Astronomers, chemists, mathematicians, and physicists. Journal of the Association for Information Science and Technology,
50(10), 929.
Google Scholar
Cahan, D. (2003). From natural philosophy to the sciences: Writing the history of nineteenth-century science. Chicago, London: University of Chicago Press.
Google Scholar
Cao, J., Xia, T., Li, J., Zhang, Y., & Tang, S. (2009). A density-based method for adaptive LDA model selection. Neurocomputing,
72(7), 1775–1781.
Article
Google Scholar
Clauset, A., Newman, M. E., & Moore, C. (2004). Finding community structure in very large networks. Physical Review E,
70(6), 066111.
Article
Google Scholar
Clauset, A., Shalizi, C. R., & Newman, M. E. (2009). Power-law distributions in empirical data. SIAM Review,
51(4), 661–703.
MathSciNet
Article
MATH
Google Scholar
Crane, D. (1965). Scientists at major and minor universities: A study of productivity and recognition. American Sociological Review,
1965, 699–714.
Article
Google Scholar
Dai, A. M., & Storkey, A. J. (2009, December). Author disambiguation: A nonparametric topic and co-authorship model. In NIPS workshop on applications for topic models text and beyond (pp. 1–4).
Ding, Y. (2011). Community detection: Topological vs. topical. Journal of Informetrics,
5(4), 498–514.
Article
Google Scholar
Evans, T. S., & Lambiotte, R. (2009). Line graphs, link partitions, and overlapping communities. Physical Review E,
80(1), 016105.
Article
Google Scholar
Galvagno, M. (2011). The intellectual structure of the anti-consumption and consumer resistance field: An author co-citation analysis. European Journal of Marketing,
45(11/12), 1688–1701.
Article
Google Scholar
Garfield, E., & Merton, R. K. (1979). Citation indexing: Its theory and application in science, technology, and humanities (Vol. 8). New York: Wiley.
Google Scholar
Girvan, M., & Newman, M. E. (2002). Community structure in social and biological networks. Proceedings of the National Academy of Sciences,
99(12), 7821–7826.
MathSciNet
Article
MATH
Google Scholar
Glänzel, W. (2012). Bibliometric methods for detecting and analysing emerging research topics. El profesional de la información,
21(1), 194–201.
Article
Google Scholar
Glänzel, W., & Thijs, B. (2011). Using ‘core documents’ for detecting and labelling new emerging topics. Scientometrics,
91(2), 399–416.
Article
Google Scholar
Griffith, B. C., Small, H. G., Stonehill, J. A., & Dey, S. (1974). The structure of scientific literatures II: Toward a macro-and microstructure for science. Social Studies of Science,
4(4), 339–365.
Google Scholar
Griffiths, T. (2002). Gibbs sampling in the generative model of latent dirichlet allocation. Technical report, Stanford University.
Griffiths, T. L., & Steyvers, M. (2004). Finding scientific topics. Proceedings of the National Academy of Sciences,
101(suppl 1), 5228–5235.
Article
Google Scholar
Hein, D. I. O., Schwind, D. W. I. M., & König, W. (2006). Scale-free networks. Wirtschaftsinformatik,
48(4), 267–275.
Article
Google Scholar
Hirsch, J. E. (2005). An index to quantify an individual’s scientific research output. Proceedings of the National Academy of Sciences of the United States of America,
102(46), 16569–16572.
Article
MATH
Google Scholar
Kuhn, T. S. (2012). The structure of scientific revolutions. Chicago, London: University of Chicago Press.
Book
Google Scholar
Lau, J. H., Grieser, K., Newman, D., & Baldwin, T. (2011). Automatic labelling of topic models. In Proceedings of the 49th annual meeting of the association for computational linguistics: human language technologies (Vol. 1, pp. 1536–1545). Association for Computational Linguistics.
Li, D., He, B., Ding, Y., Tang, J., Sugimoto, C., Qin, Z., et al. (2010). Community-based topic modeling for social tagging. In Proceedings of the 19th ACM international conference on information and knowledge management (CIKM2010), October 26–30, 2010, Toronto, Canada (pp. 1565–1568).
Li, D., Zhu, J., Ding, Y., Xin, S., Chen, S., Tang, J., Bollen, J., & Rocha, G. (2011). Adding community and dynamics to topic models. Technical Report. School of Library and Information Science, Indiana University.
Lu, K., & Wolfram, D. (2010). Geographic characteristics of the growth of informetrics literature 1987–2008. Journal of Informetrics,
4(4), 591–601.
Article
Google Scholar
Lužar, B., Levnajić, Z., Povh, J., & Perc, M. (2014). Community structure and the evolution of interdisciplinarity in slovenia’s scientific collaboration network. PLoS ONE,
9(4), e94429.
Article
Google Scholar
McCain, K. W. (1990). Mapping authors in intellectual space: A technical overview. Journal of the American Society for Information Science,
41(6), 433.
Article
Google Scholar
Meho, L. I., & Tibbo, H. R. (2003). Modeling the information-seeking behavior of social scientists: Ellis’s study revisited. Journal of the American Society for Information Science and Technology,
54(6), 570–587.
Article
Google Scholar
Merton, R. K. (1968). The Matthew effect in science. Science,
159(3810), 56–63.
Article
Google Scholar
Mimno, D., Wallach, H. M., Talley, E., Leenders, M., & McCallum, A. (2011, July). Optimizing semantic coherence in topic models. In Proceedings of the conference on empirical methods in natural language processing (pp. 262–272). Association for Computational Linguistics.
Morris, S. A., & Goldstein, M. L. (2007). Manifestation of research teams in journal literature: A growth model of papers, authors, collaboration, coauthorship, weak ties, and Lotka’s law. Journal of the American Society for Information Science and Technology,
58(12), 1764–1782.
Article
Google Scholar
Nagarajan, R., Kalinka, A. T., & Hogan, W. R. (2013). Evidence of community structure in biomedical research grant collaborations. Journal of Biomedical Informatics,
46(1), 40–46.
Article
Google Scholar
Newman, M. (2001a). Scientific collaboration networks. I. Network construction and fundamental results. Physical Review E,
64(1), 016131.
MathSciNet
Article
Google Scholar
Newman, M. (2001b). Scientific collaboration networks. II. Shortest paths, weighted networks, and centrality. Physical Review E,
64(1), 016132.
MathSciNet
Article
Google Scholar
Newman, M. (2004). Coauthorship networks and patterns of scientific collaboration. Proceedings of the National Academy of Sciences,
101(suppl 1), 5200–5205.
Article
Google Scholar
Newman, M. (2010). Networks: An introduction. New York: Oxford University Press.
Book
MATH
Google Scholar
Palla, G., Barabási, A. L., & Vicsek, T. (2007). Quantifying social group evolution. Nature,
446(7136), 664–667.
Article
Google Scholar
Price de Solla, D. J. (1963). Little science, big science. NewYork: Columbia University Press.
Google Scholar
Radicchi, F., Castellano, C., Cecconi, F., Loreto, V., & Parisi, D. (2004). Defining and identifying communities in networks. Proceedings of the National Academy of Sciences of the United States of America,
101(9), 2658–2663.
Article
Google Scholar
Ramasco, J. J., & Morris, S. A. (2006). Social inertia in collaboration networks. Physical Review E,
73(1), 016122.
Article
Google Scholar
Rosen-Zvi, M., Chemudugunta, C., Griffiths, T., Smyth, P., & Steyvers, M. (2010). Learning author-topic models from text corpora. ACM Transactions on Information Systems (TOIS),
28(1), 4.
Article
Google Scholar
Small, H. (1973). Co-citation in the scientific literature: A new measure of the relationship between two documents. Journal of the American Society for Information Science,
24(4), 265–269.
Article
Google Scholar
Small, H. (2006). Tracking and predicting growth areas in science. Scientometrics,
68(3), 595–610.
Article
Google Scholar
Sonnenwald, D. H. (2007). Scientific collaboration. Annual Review of Information Science and Technology,
41(1), 643–681.
Article
Google Scholar
Strogatz, S. H. (2001). Exploring complex networks. Nature,
410(6825), 268–276.
Article
Google Scholar
Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications (Vol. 8). Cambridge: Cambridge University Press.
Book
MATH
Google Scholar
Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of ‘small-world’ networks. Nature,
393(6684), 440–442.
Article
Google Scholar
White, H. D. (1990). Author co-citation analysis: Overview and defense. Scholarly Communication and Bibliometrics,
84, 106.
Google Scholar
White, H. D., & Griffith, B. C. (1981). Author cocitation: A literature measure of intellectual structure. Journal of the American Society for Information Science,
32(3), 163–171.
Article
Google Scholar
White, H. D., & McCain, K. W. (1998). Visualizing a discipline: An author co-citation analysis of information science, 1972–1995. Journal of the American Society for Information Science,
49(4), 327–355.
Google Scholar
Yan, E., Ding, Y., & Jacob, E. K. (2012a). Overlaying communities and topics: An analysis on publication networks. Scientometrics,
90, 499–513.
Article
Google Scholar
Yan, E., Ding, Y., Milojević, S., & Sugimoto, C. R. (2012b). Topics in dynamic research communities: An exploratory study for the field of information retrieval. Journal of Informetrics,
6(1), 140–153.
Article
Google Scholar
Zhao, D., & Strotmann, A. (2008). Author bibliographic coupling: Another approach to citation-based author knowledge network analysis. Proceedings of the American Society for Information Science and Technology,
45(1), 1–10.
Article
Google Scholar
Zhou, D., Manavoglu, E., Li, J., Giles, L. C., & Zha, H. (2006). Probabilistic models for discovering e-communities. In Proceedings of the 15th ACM international conference on world wide web, May 23–26, 2006, Edinburgh, Scotland (pp. 173–182).