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"David Knoke and Song Yang's Social Network Analysis, Third Edition provides a concise introduction to the concepts and tools of social network analysis. The authors convey key material while at the same time minimizing technical complexities. The examples are simple: sets of 5 or 6 entities such as individuals, positions in a hierarchy, political offices, and nation-states, and the relations between them include friendship, communication, supervision, donations, and trade. The new edition reflects developments and changes in practice over the past decade. The authors also describe important recent developments in network analysis, especially in the fifth chapter. Exponential random graph models (ERGMs) are a prime example: when the second edition was published, P* models were the recommended approach for this, but they have been replaced by ERGMs. Finally, throughout the volume, the authors comment on the challenges and opportunities offered by internet and social media data"--

This paper reports on the development of social network analysis, tracing its origins in classical sociology and its more recent formulation in social scientific and mathematical work. It is argued that the concept of social network provides a powerful model for social structure, and that a number of important formal methods of social network analysis can be discerned. Social network analysis has been used in studies of kinship structure, social mobility, science citations, contacts among members of deviant groups, corporate power, international trade exploitation, class structure, and many other areas. A review of the formal models proposed in graph theory, multidimensional scaling, and algebraic topology is followed by extended illustrations of social network analysis in the study of community structure and interlocking directorships.

The role that causality can play in social network analysis is unclear. The author provides a broad characterization of social network analysis before considering the nature of causality. He distinguishes four types of causality: system causality, statistical causality, mechanism causality, and algorithmic causality. Their potential places in network analysis are discussed. Understanding generative mechanisms—be they system, mechanism, or algorithmic—seems the most promising way to proceed. The role of statistical causality is a source of potential data analytic tools that can be mobilized within analyses conducted in the spirit of the other three types of causality.

In the past two decades, social network analysis (SNA) has become a major analytical paradigm in sociology and now occupies a strategic place in disciplinary debates on a wide variety of issues. Historians, however, have been slow to adopt the approach for at least three reasons. First, the conceptual orientation of sociologists practicing historical social network analysis (HSNA) remains unfamiliar to the majority of professional historians. Just when SNA was maturing in the late 1980s and 1990s, the interdisciplinary interest in social science theory among historians, so characteristic of the 1970s and early 1980s, began to wane. The subsequent turn toward post modernist thinking in history left the profession increasingly uninformed about both classical and contemporary social theory. Second, those quantitatively-oriented historians who might be predisposed to use SNA's specialized statistical methods constitute less than a quarter of the profession today, thus the risk of SNA finding its way into mainstream historical scholarship is low to start. Third, SNA's data requirements are formidable. SNA demands evidence of social interaction among all members of a social system for a variety of behaviors, and thus necessitates a broad range of high-quality records for the place, time and activities being studied. Because historians are plagued by an incomplete historical record and imperfect understandings of past social relations, HSNA remains an inherently problematic enterprise. Yet despite conceptual, methodological and evidentiary obstacles, SNA possesses real potential for historical analysis.

Social network analysis (SNA) in archaeology has become important for a range of theoretical and methodological approaches that can more generally be characterized as relational. They are relational in that it is the ties between actors (or nodes) that define social connections. Archaeologists are currently employing a diversity of theoretical approaches to networks, and the perspective taken in this review is that SNA can provide insights into a number of different social processes using different theories. Following a brief historical overview, I discuss two aspects of SNA: the structural position of the actor or node, and characterizations of whole networks. I then summarize several broad classes of archaeological networks: historical, spatial, and material. I conclude with a call for more bridging approaches to span alternative theoretical and methodological approaches in the archaeology of networks.