@article{sun2013social,
  abstract = {The birth and decline of disciplines are critical to science and society. How do scientific disciplines emerge? No quantitative model to date allows us to validate competing theories on the different roles of endogenous processes, such as social collaborations, and exogenous events, such as scientific discoveries. Here we propose an agent-based model in which the evolution of disciplines is guided mainly by social interactions among agents representing scientists. Disciplines emerge from splitting and merging of social communities in a collaboration network. We find that this social model can account for a number of stylized facts about the relationships between disciplines, scholars, and publications. These results provide strong quantitative support for the key role of social interactions in shaping the dynamics of science. While several “science of science” theories exist, this is the first account for the emergence of disciplines that is validated on the basis of empirical data.},
  author = {Sun, Xiaoling and Kaur, Jasleen and Milojevic, Stasa and Flammini, Alessandro and Menczer, Filippo},
  doi = {10.1038/srep01069},
  interhash = {5cd31392e997555d78596f962044f84b},
  intrahash = {721dcd5644cca27fd50d8e6ffd667056},
  journal = {Scientific Reports},
  month = jan,
  publisher = {Macmillan Publishers Limited},
  title = {Social Dynamics of Science},
  url = {http://dx.doi.org/10.1038/srep01069},
  volume = 3,
  year = 2013
}

@article{pham2011development,
  abstract = {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.},
  address = {Wien},
  affiliation = {Information Systems and Database Technology, RWTH Aachen University, Aachen, Ahornstr. 55, 52056 Aachen, Germany},
  author = {Pham, Manh and Klamma, Ralf and Jarke, Matthias},
  doi = {10.1007/s13278-011-0024-x},
  interhash = {193312234ed176aa8be9f35d4d1c4e72},
  intrahash = {8ae08cacda75da80bfa5604cfce48449},
  issn = {1869-5450},
  journal = {Social Network Analysis and Mining},
  keyword = {Computer Science},
  number = 4,
  pages = {321--340},
  publisher = {Springer},
  title = {Development of computer science disciplines: a social network analysis approach},
  url = {http://dx.doi.org/10.1007/s13278-011-0024-x},
  volume = 1,
  year = 2011
}

@article{borrego2012measuring,
  abstract = {This paper explores the possibility of using data from social bookmarking services to measure the use of information by academic researchers. Social bookmarking data can be used to augment participative methods (e.g. interviews and surveys) and other, non-participative methods (e.g. citation analysis and transaction logs) to measure the use of scholarly information. We use BibSonomy, a free resource-sharing system, as a case study. Results show that published journal articles are by far the most popular type of source bookmarked, followed by conference proceedings and books. Commercial journal publisher platforms are the most popular type of information resource bookmarked, followed by websites, records in databases and digital repositories. Usage of open access information resources is low in comparison with toll access journals. In the case of open access repositories, there is a marked preference for the use of subject-based repositories over institutional repositories. The results are consistent with those observed in related studies based on surveys and citation analysis, confirming the possible use of bookmarking data in studies of information behaviour in academic settings. The main advantages of using social bookmarking data are that is an unobtrusive approach, it captures the reading habits of researchers who are not necessarily authors, and data are readily available. The main limitation is that a significant amount of human resources is required in cleaning and standardizing the data.},
  author = {Borrego, Ángel and Fry, Jenny},
  doi = {10.1177/0165551512438353},
  eprint = {http://jis.sagepub.com/content/38/3/297.full.pdf+html},
  interhash = {71ddfdd5b3d99b1a2986b4ded5e02b3c},
  intrahash = {e5ccbb3378eeb88e7288d8ce59539812},
  journal = {Journal of Information Science},
  number = 3,
  pages = {297--308},
  title = {Measuring researchers' use of scholarly information through social bookmarking data: A case study of BibSonomy},
  url = {http://jis.sagepub.com/content/38/3/297.abstract},
  volume = 38,
  year = 2012
}