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Network analysis (NA) is a set of integrated techniques to depict relations among actors and to analyze the social structures that emerge from the recurrence of these relations. The basic assumption is that better explanations of social phenomena are yielded by analysis of the relations among entities. This analysis is conducted by collecting relational data organized in matrix form, rather than surveying social characteristics of individuals sampled from a larger population. Traditional sample surveys isolate each actor and reduce their social relations to individual characteristics, the purpose being to analyse statistical relations among variables. These causal relations can be only viewed as useful indicators of underlying mechanisms, which are in fact any kind of encounter and exchange among real actors displaying everyday life strategies. NA concentrates its analytical attention directly on these phenomenological encounters, while traditional surveys can only identify causal connections and are unable to explain operating mechanisms (Collins 1988). For instance, a statistical relation between social origin and status attainment fails to depict the actual mechanism of social mobility; rather, it is merely a signal of social relations among the respondent’s relatives and within their broader social milieu. It is the shape and intensity of the network of these relations that explains the persistence of, or change in, social status across generations. Although deterministic approaches usually emphasise that NA enables study of how the social structure of relationships around a person group, or organization affects behaviors and attitudes, social networks are in their turn constructed by individual or corporate actors, to the extent that they are able to mobilize social resources for the purpose of choosing partners or counterparts and organizing coalitions. In short, structurally bounded purposive actions may affect the social structure and vice versa.
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If actors are depicted as nodes, and their relations as lines among pairs of nodes, the concept of social network changes from being an evocative metaphor to an operative analytical tool which utilizes the mathematical language of graph theory and of matrix and relational algebra.
Since the mid-1970s, the development of algorithms integrated into statistical packages, and the availability of greater computational capacity in the decade that followed, enabled the successful application of these techniques to structures larger and more complex than the personal networks studied previously. The foundation of specialized journals (Social Networks and Connections) and the publication of NA manuals has helped to institutionalize this approach, which ﬁnds increasing acceptance in the literature of such disciplines as sociology, anthropology, social psychology, history, and political science, while statistics and mathematics contribute constantly to the development of analytical tools. The activity of an interdisciplinary community of scholars is evidenced by the founding of the International Network for Social Network Analysis in the late 1970s.
These developments have induced some scholars, mostly structural analysts, to identify a new scientiﬁc paradigm. Indeed, NA has features typical of what Kuhn described as a paradigm: the formation of a community of scholars which shares a special language, publishes journals, and meets regularly at conferences to discuss specialized topics concerning applications of technical tools and their theoretical interpretation. Also evident is the cumulative nature of this activity, in spite of the heterogeneity of the disciplines involved and of its ﬁelds of application. However, NA suffers from a lack of shared theoretical assumptions, and of theories derived from these assumptions. The idea that society is itself a set of relations, and that individual actors are embedded in social networks, is still too loosely deﬁned for it to become a theory. For these reasons, NA has been sometimes called a technique in search of a theory. One may accordingly regard it as a set of techniques with a shared methodological perspective, rather than as a new paradigm in the social sciences. These techniques allow to undertake substantive research which yields good descriptions of complex structures and generates theoretical interpretations.
2. Origins And Developments
The ideas that social phenomena can be interpreted as networks of relations, that society itself can be interpreted as a network, and that social action can be explained as the outcome of the constraints imposed and the opportunities afforded by social relations, have been recurrent since Simmel’s formal sociology and his studies of the relations between actors in dyads and triads. Nevertheless, it is only since the 1930s that attempts have been made in various disciplines to move from theoretical programs to empirical applications (Scott 1991).
A ﬁrst approach, inﬂuenced by Gestalt theory, was developed in the US by a group of social psychologists who had emigrated to that country from Germany. Lewin, Moreno, and Heider stressed the role played by the structure of interpersonal relations in the construction of cognitive schemes, and they studied information ﬂows and the formation of shared ideas. The main tool that they used for the description of empirical networks was the sociogram, together with analytical concepts like symmetrical-asymmetrical relation, centrality and periphery, reciprocity and cognitive balance. Another approach was directly linked with structural functionalism, and it was used to recast the classic research study on work organization in the wiring room of the Western Electric plant at Hawthorne. In that study, observation techniques drawn from anthropology had evidenced the importance of informal relations in work performance. Some years later Homans reorganized the same data in matrix form to conduct analysis very similar to what came to be called the ‘block model’ in the 1970s.
The so-called Manchester School of Social Anthropology, whose most outstanding representatives were Gluckman, Barnes, Mitchell, and Bott, also started from structural functionalism. But in contrast to the traditional emphasis on social integration and cohesion, it developed NA concepts to study conﬂict and power relations. Its shift of research subject from African communities to next-door-neighbor relations in European cities led to the discovery of the importance of informal relations in urban settings. The Manchester School applied concepts like density, connectivity, reachability, as well as parameters related to the intensity and strength of ties, but its exclusive concern with informal relations and its merely descriptive approach have contributed to the decline of the school since the 1970s.
By this time the main technical developments had moved to Harvard, where White devised mathematical concepts of structural analysis, following the pioneering work of Harary and Cartwright during the 1950s. Thereafter, the mainstream program would be to model the formal properties of social structures on the basis of graph theory and matrix algebra. The approach was, therefore, analytical and deductive rather than descriptive and inductive. Different types of graphs were deﬁned and their properties studied. Field research and applications generated statistical debate on centrality, centralization, and the identiﬁcation of cliques and other types of subgroups, while important concepts such as ‘structural equivalence’ were proposed.
The main theoretical assumption that fostered these technical developments was structural analysis, which theorized that an actor’s behavior and attitudes can be interpreted in terms of their structural position in the network. Since the end of the 1970s, however, this deterministic approach has been criticized, prompting a second generation of authors to investigate also the structural opportunities available to actors when they use relations that can be mobilized from speciﬁc positions. In sociology especially, this reorientation has yielded results pertaining both to substantive theories and to analytical models, usefully employed to suggest explanations of social phenomena.
As regards the former of these domains, different analytical types of ties, as well as concepts such as point centrality, network centralization, and structural balance have been proposed, together with numerous criteria for subgroup detection. The concept of structural equivalence introduced by Lorrain and White (1971) has been inspired directly by role theory. As regards substantive research, a number of middle range theories can be grouped into four different ﬁelds of empirical application:
(a) The relation between network structure and actors’ behavior and attitudes has evidenced that the density of ego-centered networks is correlated with mental disorders (Kadushin 1982); the efficiency of task-oriented groups depends on their structure (Bavelas 1950); members of cohesive networks tend to share common opinions (Epstein 1969); innovation diffusion is inﬂuenced by personal relations among actors (Coleman et al. 1957).
(b) The role played by networks in preserving and creating social inequalities has been investigated by research on social mobility, where macro-outcomes at the level of social stratiﬁcation and microstrategies are analyzed jointly by means of network techniques. Moreover, studies on the labor market have evidenced that people acquire information about job opportunities mainly on informal relations, and ‘strong’ or ‘weak’ interpersonal ties determine differential chances of entering employment (Granovetter 1973).
(c) The function played by networks in economic behavior has inspired studies of interlocking directorates (Stokman et al. 1985), and theoretical interpretations of the market as a structure of roles in which the strategy of each producer is based on the observed actions of all others (White 1981).
(d) The relational concept of power derived from Weber is particularly suitable for NA applications in the study of political phenomena. It has been found that in negatively connected exchange networks power is not a function of centrality, and exchange choices may generate structural transformations in the overall network because of changes in power dependency relations (Cook et al. 1983).
3. Merits, Limits, And Perspectives
NA techniques allow researchers to specify empirical indicators of relational concepts and to control ﬁeld hypotheses through the deﬁnition and measurement of traditional catch-all concepts like social structure and cohesion. The development of network analytical tools has helped to highlight the structures underlying the apparently inextricable complexity of relations among actors in everyday life. From this point of view, the NA approach has the same analytical purpose as other techniques, for instance, those developed in social mobility studies, whose main goal is to simplify descriptions and to standardize measures for use in comparison. Nevertheless, NA offers a wider range of potential applications than traditional surveys, given that it is hard to conceive a social phenomenon that cannot be analyzed as a set of relations among individual or corporate actors. Moreover, research problems can be addressed at both the micro and macro levels, without changing the research design and techniques, because corporate actors can simply be interpreted as networks of networks.
Major drawbacks counterbalance these advantages, however. It has often been pointed out that NA’s abundance of analytical and technical apparatus contrasts with the poverty of its theoretical and substantive results, which are mostly descriptive, or indeed artifacts generated by simpliﬁed simulations. While these criticisms are perhaps applicable to surveys as well, the relative scarcity of empirical results in NA largely is determined by the speciﬁc difficulties of ﬁeld research. Unlike traditional surveys based on standardized questionnaires on the opinions of anonymous respondents, the collection of relational information involves sampling problems as well as difficulties relating to the reliability of intrusive questions on personal contacts. This is why network analysts often prefer to collect data from documents and repertoires rather than ask direct questions. Nevertheless, scholars recognize that NA has contributed to the development of a number of empirically-grounded middle-range theories in a variety of ﬁelds. These new perspectives have in their turn encouraged the emergence of theoretical concepts based on relational assumptions. A promising concept is ‘social capital’ (Coleman 1988), which can be better formalized in network terms.
The availability of a complete set of structural indicators encourages network comparison in order to explain differences in space and changes over time. Two tendencies are now emerging. One is the development of statistical models which permit comparison between actual networks and models generated by probabilistic assumptions to test goodness of ﬁt. The other is the integration of static structural data with additional survey-derived information. The consequent systematic combination of relational information with individual attributes of nodes and ties has yielded promising results, thereby demonstrating that NA and traditional surveys are not necessarily mutually exclusive techniques of research.
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