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Here is another part of my thesis that you may find useful on its own. With academic references.
The two main network-specific fields are social network analysis (SNA) and network science (NS). The scientific literature also mentions network analysis (NA), mostly referring to the practices of analyzing networks, but also, by extension, to their methodological critique (figure below). It is then useful to clarify the nuances between the three notions.
Network analysis (NA) is a set of research practices that progressively stabilized on specific methodological foundations. Although there is a relative consensus on its theoretical ground, its practices are not unified. They include both what Erikson (2013) calls the formalist approach, based on a “structuralist interpretation” (networks are phenomena, e.g. in Georg Simmel’s sociology), and the relationalist approach, that “rejects [the] essentialism” of the network (as an apparatus to know, e.g. in the natural sciences).
Although NA is primarily a practice, we can also see it as a field; a field about a practice. And this practice is much older than its formalization as a field. The overview proposed by Borgatti et al. (2009) places the point of origin of NA within the social sciences (with Moreno’s sociograms, 1934), before it “radiated into a great number of fields, including physics and biology” during the nineties. For these authors, “network analysis” is not a field but a longstanding practice progressively formalized into SNA, and later, NS. However, other authors acknowledge it as an independent field (Brandes and Erlebach, 2005; Chiesi, 2015), with its own methodological knowledge derived from graph theory, and its own theoretical discussions (e.g. Barnes & Harary, 1983; Butts, 2009). Even so, NA is centered on practice. Brandes and Erlebach for instance find “adequate to treat network analysis as a field of its own” (2005). But they add that “[f]rom a computer science point of view, it might well be subsumed under ‘applied graph theory,’ since structural and algorithmic aspects of abstract graphs are the prevalent methodological determinants in many applications, no matter which type of networks are being modeled.” Similarly, for Chiesi NA “can be regarded as a set of techniques with a shared methodological perspective” (2015). NA is part of other fields, including NS and SNA, as a practice. But NA as a field is additionally concerned with the foundations of this practice. It has its own intellectual and cultural space.
Social network analysis (SNA) predates both NS and NA. Indeed, the network is a key idea to multiple schools of thought in the social sciences, from Moreno’s sociograms (1934) to White’s kinship models (1963), Milgram’s “six degrees of separation” (1967), Lévy-Strauss’ structural anthropology (1973) and Granovetter’s “strength of weak ties” (1973). These long and rich considerations on the relational nature of the social coalesced into the field of SNA. In accordance with this thick heritage, the field sustains an in-depth discussion on the empirical nature of the networks it studies, and pays close attention to the various methodological issues tied to the use of its instruments.
Network science (NS) emerged during the late nineties as a “highly interdisciplinary research area” (Börner et al., 2007; see also Barabási, 2016; Hidalgo, 2016) around the object of complex network. Graph theory is generally presented as its point of origin, and more precisely the random graph model (Erdős & Renyi, 1960). As scholars across various disciplines realized that their empirical networks were usefully described by the newly formalized concept of complex network, the theories of NS disseminated as an operational toolkit for analyzing networks. It is worth mentioning that the apparition of the web, and later social media, provided plenty of network data that called for a democratization of network analysis methods. Epistemic clashes in network science features an in-depth inquiry into the epistemic foundations of the field, including a presentation of its key concepts and an analysis of its main controversy.
Network science (NS), social network analysis (SNA), and network analysis (NA) are three distinct domains. Such similar names are unfortunate, because it downplays important differences. I acknowledge the profound intrication of the fields; my attempt to make the distinction is not about enforcing a clear demarcation between them. I rather aim at clarifying the fringe of knowledge and practices that are, in each field, incompatible with the other two. Indeed, despite their important overlap, key specificities subsist, that notably explain that NA resists dissolving into NS and/or SNA.
Differences between NA and SNA. They boil down to the fact that from the perspective of NA, a network is a set of inscriptions. NA is concerned with networks-as-data: it can be used with potentially any data set, as long as it is formatted as a network. Thus it differs from SNA on two notable points: (1) NA is also interested in non-social networks (e.g. protein-gene interactions), and (2) it is not concerned with the gap between a social phenomenon and its reduction as a network. Since SNA is about networks-as-phenomena, it is deeply concerned about the part of reality that is left aside datafied networks; while for NA the datafied network is a given. This is not to say that networks-as-data can dodge the ordeal of empirical validity, but rather that this discussion takes place outside of NA, in the field where empirical data come from (e.g. molecular biology).
Differences between NA and NS. Contrary to NS, NA aims at just describing networks. NS is a broad field with its own subcultures and practices, relatively united around the notion of complex network; it comes with its own research questions such as: Can we find universal laws capable of explaining the pervasiveness of complex networks? NA, by contrast, is quite agnostic in terms of research questions. It focuses instead on how to describe and account for a given network. Analyzing networks is part of what network scientists do, of course, but NA also extends beyond the domain defined by the research questions of NS. An idiographic account of a particular network does not typically meet the publication criteria of NS, contrary to media studies (e.g. Áragon et al., 2013), sociology (e.g. Adamic and Glance, 2005), or digital humanities (e.g. Grandjean, 2016). As an example, the journal Network Science (Brandes et al., 2013) publishes many different types of papers, part of which are case-based, but none of them are just empirical accounts.
The overlap between NA, NS and SNA. While the three fields aim to accomplish different things, they overlap in how they deal with networks. The algorithms and metrics used are largely the same, despite the presence of some specificities in each field. On a practical level, the three fields can easily meet. For instance, within the computational social sciences (Lazer et al., 2009) intersect (1) the figure of the complex network and the practice of modeling, characteristic of NS, (2) the knowledge on the social of SNA, and (3) the empirical practice of NA. The techniques developed by NS disseminated to SNA, but as Freeman (2008) narrates, some methods also travelled the other way around. This important overlap makes it tempting to simplify the situation by assuming that one of the fields subsumes or otherwise contains the other two (pick your favorite!). But doing so would just make us blind to the epistemic trouble caused, at the fringe of each field, by its peculiarities.
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