The founding fathers of bibliometrics (De Solla Price, 1963; Garfield, 1967; Nalimov, 1969) on the one hand, and historians and sociologists of science on the other (from Merton to the relativist  school), have emphasized the network based organisation of the scientific community. The former introduced the idea of networks of articles and authors based on bibliographical references (citations) as the central pillar of a dynamic study of science. In the wake of this, the collaborative relationships between authors have inspired a vast literature, starting from DeB. Beaver & Rosen (1978) or Frame & Carpenter (1979). The French relativist school following Bloor (1976) began by concentrating on actors and lexical signals (Callon et al., 1986) before promoting a general actor-network theory. The basic triangle of actors/ contents/ articles and associated networks allows for an in-depth quantitative description of scientific phenomena, a large part of the relevant information being electronically available in bibliographic databases. (Zitt, Michel & Bassecoulard, Elise, S&T networks and bibliometrics: The case of international scientific collaboration, 4th Congress on Proximity Economics: Proximity, Networks and Co-ordination, Marseille, France, 2004)

According to this model, the work of scientists consists of the enrolment and juxtaposition of heterogeneous elements — rats, test-tubes, colleagues, journal articles, grants, papers at scientific conferences, and so on — which need continual management. Scientists' work is the simultaneous reconstruction of social contexts of which they form a part — labs simultaneously rebuild and link the social and natural contexts upon which they act. Examining inscriptions is a major approach to ANT. The other is to "follow the actor", via interviews and ethnographic research. Inscriptions include journal articles, conference papers, presentations, grant proposals, and patents. Incriptions are the major product of scientific work (Callon et al., 1986; Latour, Bruno & Woolgar, Steve, La vie de laboratoire : la production des faits scientifiques, La Découverte, Paris, 1988). Chapter 5 includes a description of co-word analaysis, which was originally developed for analyzing inscriptions. (Chen, Chaomei, Mapping Scientific Frontiers: The Quest for Knowledge Visualization, Springer, London, 2003 pp. 32-33)

Beyond aggregated dsitributions, close-up analyses of networks have been pioneered on the citation side by Crane (1972), Garfield (1970), Kessler (1963), and Small and Griffith (1974), as well as on the lexical side, with the coword analysis promoted by the Anglo-French sociology of innovation (Callon, Courtial, Turner, & Bauin, 1983). They are now being renewed by social network analysis, which helps to characterize both local and global properties of networks—for example, the small world (Watts & Strogatz, 1998) structure. Self-similarity, found in most scientific networks and already suggested by aggregate distributions of node degrees, is often put down to self-organization of scientific communities (see, e.g., Katz, 1999). (Zitt, Michel, Facing Diversity of Science: A Challenge for Bibliometric Indicators, in 3 Measurement, 1, 38--49 (2005), http://www.obs-ost.fr/doc_attach/FacingDiversityOfScience.pdf )

One day later [June 1960], Allen sent his set of biochemistry articles together with a drawing of the citation relationships between them. Gordon Allen had, in other words, drawn the first citation network: "The arrows indicate the direction in which one would be led in a conventional literature search, starting at any point on the network. A citation index would permit one to trace the arrows in the opposite direction, and hence to find all the articles no matter where on the network he started." He emphasized that this small network was an extract from “a considerably more voluminous literature on the same topic, all tied together with citations”. Garfield reacted strongly: "The material you sent me is magnificent! This must have been a great deal of work. It is fabulous. Why didn’t we think to do this before. I didn’t have this in mind when I said I had some examples of the power of the Citation Index. I merely meant specific articles which could be traced through a CI. (...) I once had the idea that some type of network theory could be used with Citation Indexes. I am now convinced more than ever, from your example, that this will be true." (Wouters, Paul, The Citation Culture, Universiteit van Amsterdam, mar, 1999, http://garfield.library.upenn.edu/wouters/wouters.pdf  p. 51 + fig. 2.2 p. 54)

Bollen (Bollen, John & Van de Sompel, Herbert & Smith, Joan & Luce, Rick, Toward alternative metrics of journal impact: A comparison of download and citation data, in submitted Information Processing and Management (2005), http://arxiv.org/abs/cs.DL/0503007 ) suggests to consider an impact factor which a) is to be calculated from both usage data and citation data (not only form the latter); b) would be calculated by metrics based on the topological structure of the existing journal network (of citations and usage, i.e. downloading). These metrics, known as "social network analysis", allow to obtain more significant impact measures, which consider the topological position of a journal inside the network instead of being based only on the number of citations received. (Di Donato, F., Designing a Semantic Web Path to e-Science, , 2005, http://eprints.rclis.org/archive/00005139/ )