Stanley Wasserman

" Social network analysis is used widely in the social and behavioral sciences, as well as in economics, marketing, and industrial engineering. The social network perspective focuses on relationships among social entities and is an important addition to standard social and behavioral research, which is primarily concerned with attributes of the social units. Social Network Analysis: Methods and Applications reviews and discusses methods for the analysis of social networks with a focus on applications of these methods to many substantive examples. It is a reference book that can be used by those who want a comprehensive review of network methods, or by researchers who have gathered network data and want to find the most appropriate method by which to analyze it. It is also intended for use as a textbook as it is the first book to provide comprehensive coverage of the methodology and applications of the field."--Publisher's description.

Models and Methods in Social Network Analysis, first published in 2005, presents the most important developments in quantitative models and methods for analyzing social network data that have appeared during the 1990s. Intended as a complement to Wasserman and Faust's Social Network Analysis: Methods and Applications, it is a collection of articles by leading methodologists reviewing advances in their particular areas of network methods. Reviewed are advances in network measurement, network sampling, the analysis of centrality, positional analysis or blockmodelling, the analysis of diffusion through networks, the analysis of affiliation or'two-mode'networks, the theory of random graphs, dependence graphs, exponential families of random graphs, the analysis of longitudinal network data, graphical techniques for exploring network data, and software for the analysis of social networks.

Spanning nearly sixty years of research, statistical network analysis has passed through (at least) two generations of researchers and models. Beginning in the late 1930's, the first generation of research dealt with the distribution of various network statistics, under a variety of null models. The second generation, beginning in the 1970's and continuing into the 1980's, concerned models, usually for probabilities of relational ties among very small subsets of actors, in which various simple substantive tendencies were parameterized. Much of this research, most of which utilized log linear models, first appeared in applied statistics publications. But recent developments in social network analysis promise to bring us into a third generation. The Markov random graphs of Frank and Strauss (1986) and especially the estimation strategy for these models developed by Strauss and Ikeda (1990; described in …

This paper is a revision of a paper entitled" An Approach to the Study of Structural Change" presented at the Annual Meetings of the American Sociological Association, August 30-September 3, 1986, New York, NY. We are grateful to Dawn lacobucci for her research assistance, and we thank Gloria DeWolfe for typing the manuscript. We also thank Marshall W. Meyer and three anonymous ASO referees for their helpful comments. Support for this research was provided by National Science Foundation grants# SES 80-08570 and# SES 83-19364 to the University of Minnesota and# SES 84-08626 to the University of Illinois at Urbana-Champaign. Support was also provided by the Program on Nonprofit Organizations, Yale University.The paper explores DiMaggio and Powell's thesis that under conditions of uncertainty organizational decision makers will mimic the behavior of other organizations in their environment. We add to their discussion by positing that managers are especially likely to mimic the behavior of organizations to which they have some type of network tie via boundary-spanning personnel. Data are presented on the charitable contributions of 75 business corporations to 198 nonprofit organizations in the Minneapolis-St. Paul metropolitan area in 1980 and 1984. Using logistic regression models, we found that a firm is likely to give more money to a nonprofit that was previously funded by companies whose CEOs and/or giving officers are known personally by the firm's boundary-spanning personnel. Firms are also likely to give greater contributions to a nonprofit that is viewed more favorably by the local philanthropic elite. We also …

A new method was devised to test object permanence in young infants. Five- month-old infants were habituated to a screen that moved back and forth through a 180-degree arc, in the manner of a drawbridge. After infants reached habituation, a box was centered behind the screen. Infants were shown two test events: a possible event and an impossible event. In the possible event, the screen stopped when it reached the occluded box; in the impossible event, the screen moved through the space occupied by the box. The results indicated that infants looked reliably longer at the impossible than at the possible event. This finding suggested that infants (1) understood that the box continued to exist, in its same location, after it was occluded by the screen, and (2) expected the screen to stop against the occluded box and were surprised, or puzzled, when it failed to do so. A control experiment in which the box was placed …

Social network analysis, a method for analyzing relationships between social entities, has expanded over the last decade as new research has been done in this area. How can these new developments be applied effectively in the behavioral and social sciences disciplines? In Advances in Social Network Analysis, a team of leading methodologists in network analysis addresses this issue. They explore such topics as ways to specify the network contents to be studied, how to select the method for representing network structures, how social network analysis has been used to study interorganizational relations via the resource dependence model, how to use a contact matrix for studying the spread of disease in epidemiology, and how cohesion and structural equivalence network theories relate to studying social influence. It also offers statistical models for social support networks. Advances in Social Network Analysis is useful for researchers involved in general research methods and qualitative methods, and who are interested in psychology and sociology.

Network forms of organization, unlike hierarchies or marketplaces, are agile and are constantly adapting as new links are added and dysfunctional ones dropped. We review some of the theoretical and methodological accomplishments and challenges of contemporary research on organizational networks. We then offer an analytic framework that can be used to specify and statistically test simultaneously multilevel, multitheoretical hypotheses about the structural tendencies of organizational networks. We conclude with an empirical study illustrating some of the capabilities of this framework.

This special topic forum, commissioned to stimulate theory development on building effective networks, contains eleven papers spanning the micro, meso, macro, and meta levels of analysis. Each paper breaks new ground; collectively, they suggest that we are at a crossroads in network research. Important opportunities remain, however, for further work in network theory development, and we highlight major gaps relating to network theory's scope and mission, accessibility, integration with other perspectives, and attention to process and internationalization issues.

The research described here builds on our previous work by generalizing the univariate models described there to models for multivariate relations. This family, labelled p*, generalizes the Markov random graphs of Frank and Strauss, which were further developed by them and others, building on Besag's ideas on estimation. These models were first used to model random variables embedded in lattices by Ising, and have been quite common in the study of spatial data. Here, they are applied to the statistical analysis of multigraphs, in general, and the analysis of multivariate social networks, in particular. In this paper, we show how to formulate models for multivariate social networks by considering a range of theoretical claims about social structure. We illustrate the models by developing structural models for several multivariate networks.

A major criticism of the statistical models for analyzing social networks developed by Holland, Leinhardt, and others [Holland, P.W., Leinhardt, S., 1977. Notes on the statistical analysis of social network data; Holland, P.W., Leinhardt, S., 1981. An exponential family of probability distributions for directed graphs. Journal of the American Statistical Association. 76, pp. 33–65 (with discussion); Fienberg, S.E., Wasserman, S., 1981. Categorical data analysis of single sociometric relations. In: Leinhardt, S. (Ed.), Sociological Methodology 1981, San Francisco: Jossey-Bass, pp. 156–192; Fienberg, S.E., Meyer, M.M., Wasserman, S., 1985. Statistical analysis of multiple sociometric relations. Journal of the American Statistical Association, 80, pp. 51–67; Wasserman, S., Weaver, S., 1985. Statistical analysis of binary relational data: Parameter estimation. Journal of Mathematical Psychology. 29, pp. 406–427; Wasserman, S …

Loglinear models are adapted for the analysis of multivariate social networks, a set of sociometric relations among a group of actors. Models that focus on the similarities and differences between the relations and models that concentrate on individual actors are discussed. This approach allows for the partitioning of the actors into blocks or subgroups. Some ideas for combining these models are described, and the various models and computational methods are applied to the analysis of data for a corporate interlock network of the 25 largest organizations in Minneapolis/St. Paul and for a classic network of 18 monks in a cloister. The computational techniques all involve variations on the standard iterative proportional-fitting algorithm used extensively in the analysis of multidimensional contingency tables.

The use of log linear models to summarize and describe categorical data in the form of multiple cross-classifications became increasingly popular in the 1970s. The work of Goodman (see Goodman, 1972) and books by Bishop, Fienberg, and Holland (1975), Fienberg (1980), Haberman (1978, 1979), and Upton (1978) have

This paper generalizes thep* model for dichotomous social network data (Wasserman & Pattison, 1996) to the polytomous case. The generalization is achieved by transforming valued social networks into three-way binary arrays. This data transformation requires a modification of the Hammersley-Clifford theorem that underpins thep* class of models. We demonstrate that, provided that certain (non-observed) data patterns are excluded from consideration, a suitable version of the theorem can be developed. We also show that the approach amounts to a model for multiple logits derived from a pseudo-likelihood function. Estimation within this model is analogous to the separate fitting of multinomial baseline logits, except that the Hammersley-Clifford theorem requires the equating of certain parameters across logits. The paper describes how to convert a valued network into a data array suitable for fitting the …

This article presents a new methodology for studying a social network of interpersonal relationships, based on stochastic modeling of the changes that occur in the network over time. Specifically, we postulate that these changes can be modeled as a continuous-time Markov chain. The transition rates for the chain are dependent on a small set of parameters that measure the importance of various aspects of social structure on the probability of change. We discuss the assumptions of the framework and describe two simple models that are applications of it. We then present, analyze, and interpret several examples, and we outline methods of parameter estimation. The models prove to be quite effective and allow us to better understand the evolution of a network.