As indicated by my rating, I found Understanding Social Networks to be an excellently written and useful book. I will provide an overview of its approach, and offer a couple of concrete examples of matters treated by its author that were of special interest to me. By so doing, I hope to give prospective readers at least a taste of the quality and style of this book.
Understanding Social Networks begins by with the introduction of basic network ideas, adding complexity as it goes. Kadushin artfully weaves in the most important findings of small group theory as he develops his presentation, and supplies clear and fascinating examples to illustrate the concepts. A reader who is comfortable with high school algebra will find the math in this book to be pretty easy: Kadushin avoids dealing with theorems, algorithms, and computations. This is as it should be. What this book clarifies is the “object” of social networking theory. A student who is interested in learning about social network theory is better advised to start with a book such as this one; the math can be learned later. Besides, there are other books, some quite good, which focus on the math.
My first serious encounter with network theory took place back in the late 1980s. I was actually studying operations research at that time, and needed to know graph theory, the mathematical basis of networking theory. In the process, I stumbled into some materials concerning “structural balance,” a social networking concept. On my first encounter with this material, I flippantly (and naively) concluded that, if one had the data to build a graph and then to determine whether it was “balanced,” one would already know everything of value that the graph-theoretic model could contain. After reading Kadushin’s book, I abandoned this view. Network analysis can uncover things that are very hard (or impossible) to see with the naked eye, as it were. It has proved to be especially useful in marketing and other research, especially in this, the age of Facebook and Google. And of course, anthropologists and epidemiologists have long found network analysis to be essential in studies of the diffusion of cultural elements and diseases, respectively. (Kadushin addresses these topics in Chapter 9.)
Understanding Social Networks made me look at familiar things in new and stimulating ways. For example, when I worked as a management consultant I always sought out the “informal” leaders in an organization. Network analysis reveals the ubiquity of such informal leaders as a matter of fact. That is to say, when I found informal leaders in every organization I have ever encountered, it wasn’t just luck. Formal organizations do not—and could not—get their work done if the formal network, represented by an organizational chart, wasn’t supplemented by the informal networks which inevitably arise in any organization. Informal networks always have informal leaders, so they are inevitable, too. Kadushin, drawing on a comprehensive body of social theory and research (check out his bibliography), provides a convincing explanation, based on basic human drives, as to why this is so. Though there are methodological challenges to carrying it out, network analysis can identify these leaders, who are often invisible to upper management and outside observers.
Just as important as identifying natural or informal leaders is the characterization of what makes them leaders. Summarizing research done on that topic, Kadushin concludes that effective leaders, whether “informal” or “formally appointed,” are more likely to initiate interactions with people of lower status in their hierarchies than are other high-status members. Let me clarify: High status members of groups tend to receive more interactions from others than they reciprocate or initiate themselves. They are less likely to reciprocate interactions with lower-status members of their groups. But those who become leaders (as opposed to "bosses" and "snobs") follow a different pattern. They initiate interactions with lower-status members, and are more likely to respond to interactions initiated by them. These patterns show up in network models and in the “centrality scores” of the unheralded leaders that exist in every organization. Effective “formal” leaders follow the same patterns. Thus, network analysis provides a way to model the importance of relationship structure to leadership effectiveness. If you want to be an effective leader, you will avoid snubbing even the humblest people in your domain. Of course, this would not in itself constitute a guide to leadership. But much contemporary work on leadership puts high emphasis on relationship building, and the evidence presented by Kadushin strongly supports this.
The examples I have just shared only scratch the surface of this useful and thought-provoking book. I should add that he manages to accomplish this pleasant combination of theory and technique in just over 200 pages. That in itself should make the book a first choice for those who want to explore social networking theory.
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