Studying Social Networks (PDF)

connecting line segments. Metaphors are very useful for memorization and creative thinking. However, it is not neces- sarily obvious which aspects of a metaphor correspond to actual proper- ties of that which is represented, and which aspects do not. Another pitfall of metaphors and models alike is the use of similar rep- resentations for weakly related phenomena. By abstracting from the non- essential (with respect to a specific perspective), otherwise invalid com- monalities and conclusions may emerge. To illustrate this point, consider (statistical) "distributions" as another example of a representation. If both the distribution of life-expectancy in the east of Austria and the household income in a suburb of Berlin are unimodal (i.e., have a single peak), does this imply that there is a relation between these two phenomena? We as- sume that you would not think so, but it appears to be much more tempt- ing to speculate about such relations when two networks exhibit similar features because it is more easily forgotten that they are simplifying and homogenizing, reductionist representations. The study of social networks is, hence, the study of a particular type of representation in social science contexts (Freeman 1989). Therefore, social networks are constructs and do not exist as such. They are repre- sentations, in which aspects of a social phenomenon - aspects that seem to be relevant in a specific context and for a specific purpose - are expressed in ways more amenable to scientific scrutiny. Since there are no social networks per se, it is a linguistic simplification when we say that we are studying social networks. In fact we are studying social phenomena by means of network representations. This is carried It appears that the term "social network" was coined in Barnes (1954), in which pre- cisely this image is evoked out by gathering data about aspects of a phenomenon and organizing the data in a convenient form, by applying methods that produce additional, derived data, and translating these back to the realm of the phenomenon. Clearly, this is no different from other empirical investigations. What is distinct in network analysis, however, are the kinds of data and methods, and the reasoning that motivates network representations and justifies the interpretation of results.

. . . . . . . . . . . . . . . . .83 3.2.3Alter Recall . . . . . . . . . . . . . . . . . . . . . . . . .85 3.3Quality Issues . . . . . . . . . . . . . . . . . . . . . . . . . . . . .93 3.4Ethical Considerations . . . . . . . . . . . . . . . . . . . . . . .97 3.5Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .99 3.6Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100 4. Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103 4.1Mathematical Representation . . . . . . . . . . . . . . . . . . . 104 4.1.1Graphs . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106 4.1.2Ego-Centered Networks . . . . . . . . . . . . . . . . . 109 4.1.3Two-Mode Networks . . . . . . . . . . . . . . . . . . . 110 4.2Indexing and Grouping . . . . . . . . . . . . . . . . . . . . . . . 111 4.2.1Dyads as the Unit of Analysis . . . . . . . . . . . . . . 112 4.2.2Network Characteristics . . . . . . . . . . . . . . . . . 118 4.2.3Centrality . . . . . . . . . . . . . . . . . . . . . . . . . . 123 4.2.4Cohesion . . . . . . . . . . . . . . . . . . . . . . . . . . . 130 4.2.5Roles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134 4.2.6Blockmodeling . . . . . . . . . . . . . . . . . . . . . . . 137 4.3Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140 4.3.1Idealized Models . . . . . . . . . . . . . . . . . . . . . . 141 4.3.2Exponential-Family Random Graph Models . . . . 143 4.4Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145 4.5Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146 5. Visualization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149 5.1Graphical Representation . . . . . . . . . . . . . . . . . . . . . 151 5.1.1Sociogram . . . . . . . . . . . . . . . . . . . . . . . . . . 151 5.1.2Sociomatrix . . . . . . . . . . . . . . . . . . . . . . . . . 155 5.1.3Two-Mode Network Representations . . . . . . . . 159 5.2Multivariate Information Visualization . . . . . . . . . . . .162 5.2.1Substance-Based Layout . . . . . . . . . . . . . . . . .165 5.2.2Other Graphical Variables . . . . . . . . . . . . . . . .169 5.3Information Layering . . . . . . . . . . . . . . . . . . . . . . . . 169 5.3.1Filtering . . . . . . . . . . . . . . . . . . . . . . . . . . . 170 5.3.2Level of Detail . . . . . . . . . . . . . . . . . . . . . . . 174 5.3.3Micro/Macro Reading . . . . . . . . . . . . . . . . . . 175 5.4Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177 5.5Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 179 6. Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183 List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 189 List of Boxes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 191 About the Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 193 Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 195 Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 211