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+7 495 772 95 90 *12232
Address: 3 Krivokolenny pereulok, room 330
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ORCID: 0000-0002-2378-8860
ResearcherID: J-6918-2013
Scopus AuthorID: 7102046306
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Consultation hours
Friday 8-10 p.m.
V. Kuskova
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Stanley Sholom Wasserman

  • Stanley Sholom Wasserman has been at HSE since 2014.

Courses (2016/2017)



Journal Issues

Wasserman, S., & Galaskiewicz, J. (1993) (eds). Special issue on “Advances in Sociology from Social Network Analysis”. Sociological Methods & Research, Volume 22, Number 1.

Parkhe, A., Ralston, D., & Wasserman, S. (2006) (eds). Special issue on “Building E↵ective Networks”. Academy of Management Review, Volume 31.

Wasserman, S., & Steinley, D.S. (2011) (eds). Special issue on “Statistical Analysis and Data Mining of Networks”. Statistical Analysis and Data Analysis, Volume 4, Issue 5.


Wasserman, S. (1977). Random directed graph distributions and the triad census in social networks. Journal of Mathematical Sociology, 5, 61–86.

Wasserman, S. (1977). A survey of mathematical models for graphs. Proceedings of the 1977 American Statistical Association Social Statistics Section, II, 860–864.

Leinhardt, S., & Wasserman, S. (1978). Quantitative methods for public management: An introductory course in statistics and data analysis. Policy Analysis, 4, 549–575.

Leinhardt, S., & Wasserman, S. (1978). Exploratory data analysis: An introduction to selected methods. In K. Schuessler (ed)., Sociological Methodology 1979, (pages 311–365). San Francisco: Jossey-Bass.

Wasserman, S. (1978). Models for binary directed graphs and their applications. Advances in Applied Probability, 10, 803–818.

Leinhardt, S., & Wasserman, S. (1979). Teaching regression: An exploratory approach. The American Statistician, 33, 196–203.

Wasserman, S. (1979). A stochastic model for directed graphs with transition rates determined by reciprocity. In K. Schuessler (ed)., Sociological Methodology 1980, (pages 392–412). San Francisco: Jossey-Bass.

Fienberg, S.E., & Wasserman, S. (1979). Categorical data analysis of directed graphs: Models for a single generator. Proceedings of the 1979 American Statistical Association Social Statistics Section, 407–412.

Vickers, Z.M., & Wasserman, S. (1979). Sensory qualities of food sounds based on individual perceptions. Journal of Texture Studies, 10, 319-332.

Fienberg, S.E., & Wasserman, S. (1980). Methods for the analysis of data from multivariate directed graphs. Proceedings of the Conference on Recent Developments in Statistical Methods and Applications, (pages 137–161). Taipei, Taiwan: Institute of Mathematics, Academia Sinica.

Runger, G., & Wasserman, S. (1980). Longitudinal analysis of friendship networks. Social Networks, 2, 143-154.

Wasserman, S. (1980). Analyzing social networks as stochastic processes. Journal of the American Statistical Association, 75, 280–294.

Fienberg, S.E., & Wasserman, S. (1981). Categorical data analysis of single sociometric relations. In S. Leinhardt (ed.), Sociological Methodology 1981, (pages 156–192). San Francisco: Jossey-Bass.

Fienberg, S.E., Meyer, M., & Wasserman, S. (1981). Analyzing data from multivariate directed graphs: An application to social networks. In V. Barnett (ed.), Interpreting Multivariate Data, (pages 289-306). London: John Wiley & Sons.

Fienberg, S.E., & Wasserman, S. (1981). Comment on ‘An exponential family of probability distributions for directed graphs’, by P. W. Holland and S. Leinhardt. Journal of the American Statistical Association, 76, 54-57.

Galaskiewicz, J., & Wasserman, S. (1981). A dynamic study of change in a regional corporate network. American Sociological Review, 46, 475-484.

Dressler, J., Thompson, P.N., & Wasserman, S. (1982). E↵ect of legal education upon perceptions of crime seriousness: A response to Rummel v. Estelle. Wayne Law Review, 28, 1247-1300.

Wasserman, S. (1983). Distinguishing between stochastic models of heterogeneity and contagion. Journal of Mathematical Psychology, 27, 201-215.

Wasserman, S., & Galaskiewicz, J. (1984). Some generalizations of p1: External constraints, interactions, and non-binary relations. Social Networks, 6, 177–192.

Baillargeon, R., Spelke, E., & Wasserman, S. (1985). Object permanence in the five-month-infant. Cognition, 20, 191–208.

Fienberg, S., Meyer, M., & Wasserman, S. (1985). Statistical analysis of multiple sociometric relations. Journal of the American Statistical Association, 80, 51–67.

Galaskiewicz, J., Wasserman, S., Rauschenbach, B., Bielefeld, W., & Mullaney, P. (1985). The influence of class, status, and market position on corporate interlocks in a regional network. Social Forces, 64, 403–431.

Wasserman, S., & Weaver, S. (1985). Statistical analysis of binary relational data: Parameter estimation. Journal of Mathematical Psychology, 29, 406–427.

Wasserman, S., & Iacobucci, D. (1986). Statistical analysis of discrete relational data. British Journal of Mathematical and Statistical Psychology, 39, 41–64.

Weaver, S. O., & Wasserman, S. (1986). RELTWO – Interactive loglinear model fitting for pairs of sociometric relations. Connections: Bulletin of the International Network for Social Network Analysis, 9, 38–46.

Iacobucci, D., & Wasserman, S. (1987). Dyadic social interactions. Psychological Bulletin, 102, 293–306.

Wasserman, S. (1987). Conformity of two sociometric relations. Psychometrika, 52, 3–18.

Wasserman, S., & Anderson, C. (1987). Stochastic a posteriori blockmodels: Construction and assessment. Social Networks, 9, 1–36.

Iacobucci, D., & Wasserman, S. (1988). A general framework for the statistical analysis of sequential dyadic interaction data. Psychological Bulletin, 103, 379–390.

Wasserman, S., & Iacobucci, D. (1988). Sequential social network data. Psychometrika, 53, 261–282.

Galaskiewicz., & Wasserman, S. (1989). Mimetic and normative processes within an interorganizational field: An empirical test. Administrative Science Quarterly, 34, 454–480.

Wasserman, S., & Bockenholt, U. (1989). Bootstrapping: Applications to psychophysiology. Psychophysiology (Invited paper), 26, 208–221.

Wasserman, S., & Faust, K. (1989). Canonical analysis of the composition and structure of social networks. In Clogg, C. (ed.) Sociological Methodology, 1989, (pages 1–42). Cambridge, MA: Basil Blackwell.

Galaskiewicz, J., & Wasserman, S. (1990). Social action models for the study of change in organizational fields. In Weesie, J., and H. Flap (eds.) Social Networks Through Time, (pages 1–30). Utrecht, Netherlands: University of Utrecht/ISOR Press.

Iacobucci, D., & Wasserman, S. (1990). Social networks with two sets of actors. Psychometrika, 55, 707–720.

Wasserman, S., Faust, K., & Galaskiewicz, J. (1990). Correspondence and canonical analysis of relational data. Journal of Mathematical Sociology, 15, 11–64.

Wasserman, S., & Davis, J.H. (1991). A methodology for comparing predictions from many models to few data. Quality and Quantity, 25, 189–209.

Wasserman, S., & Iacobucci, D. (1991). Statistical modeling of one-mode and two-mode networks: Simultaneous analysis of graphs and bipartite graphs. British Journal of Mathematical and Statistical Psychology, 44, 13–44.

Anderson, C.J., Wasserman, S., & Faust, K. (1992). Building stochastic blockmodels. Social Networks (Invited Paper), 14, 137–161. Also reprinted in Scott, J. (ed). Social Networks: Critical Concepts in Sociology., Volume 2 (pages 227–247). London: Routledge.

Faust, K., & Wasserman, S. (1992a). Blockmodels: Interpretation and evaluation. Social Networks (Invited Paper), 14, 5–61.

Faust, K., & Wasserman, S. (1992b). Centrality and prestige: A review and synthesis. Journal of Quantitative Anthropology, 4, 23–78.

Faust, K., & Wasserman, S. (1993). Correlation and association models for studying measurements on ordinal relations. In Marsden, P.V. (ed.) Sociological Methodology, 1993, (pages 177–215). Cambridge, MA: Basil Blackwell.

Galaskiewicz, J., & Wasserman, S. (1993). Social Network Analysis: Concepts, methodology, and directions for the 90’s. Sociological Methods & Research (Invited Paper), 22, 3–22.

Walker, M., Wasserman, S., & Wellman, B. (1993). Statistical models for social support networks. Sociological Methods & Research (Invited Paper), 22, 71–98.

Galaskiewicz, J., & Wasserman, S. (1994). Advances in the social and behavioral sciences from social network analysis. In Wasserman, S., and Galaskiewicz, J. (eds.) Advances in Social Network Analysis: Research from the Social and Behavioral Sciences, (pages xi–xvii). Newbury Park, CA: Sage Publications.

Koehly, L., & Wasserman, S. (1994). STOCENT and STOCENTD: Stochastic centrality and prestige for actors in a social network. Connections: Bulletin of the International Network for Social Network Analysis, 17, 35-44.

Wasserman, S. (1994). Discussion of “Epidemics: Models and Data”, by Mollison, D., Isham, V., and Grenfell, B. Journal of the Royal Statistical Society, Series A (Invited Paper), 157, 144.

Anderson, C. J., &Wasserman, S. (1995). Log multiplicative models for valued social relations. Sociological Methods & Research (Invited Paper), 24, 96–127.

Pattison, P.E., & Wasserman, S. (1995). Constructing algebraic models for local social networks using statistical methods. Journal of Mathematical Psychology, 39, 57–72.

Koehly, L., & Wasserman, S. (1996). Classification of actors in a social network based on stochastic centrality and prestige. Journal of Quantitative Anthropology, 6, 75–99.

Wasserman, S., & Pattison, P.E. (1996). Logit models and logistic regressions for social networks: I. An introduction to Markov random graphs and p⇤. Psychometrika, 60, 401–425.

Wasserman, S. (1997). Nominal data. In Armitage, P., and Colton, T. (eds.), Encyclopedia of Biostatistics, Volume 4, 3001–3003. Chichester, UK: John Wiley & Sons.

Althoff, R., Cohen, N.J., McConkie, G., Wasserman, S., Maciukenas, M., Azen, R., & Romine, L. (1998). Eye movement–based memory assessment. In Becker, W., Deubel, H., and Mergner, T. (eds). Current Oculomotor Research: Physiological and Psychological Aspects, pages 293–302. New York: Plenum Publishers.

Crouch, B., Wasserman, S., & Contractor, N. (1998). A practical guide to fitting p⇤ social network models via logistic regression. Connections, 21, 87–101.

Heald, M., Contractor, N., Koehly, L., & Wasserman, S. (1998). Personal and emergent predictors of coworkers’ perceptual congruence on an organization’s social structure. Human Communication Research, 24, 536–563.

Anderson, C.J., Wasserman, S., & Crouch, B. (1999). A p⇤ primer: Logit models for social networks. Social Networks, 21, 37–66.

Pattison, P.E., & Wasserman, S. (1999). Logit models and logistic regressions for social networks: II. Multivariate relations. British Journal of Mathematical and Statistical Psychology, 52, 169–193.

Robins, G., Pattison, P.E., & Wasserman, S. (1999). Logit models and logistic regressions for social networks: III. Valued relations. Psychometrika, 64, 371–394.

Pattison, P.E., Wasserman, S., Robins, G., & Kanfer, A.M. (2000). Statistical evaluation of algebraic constraints for social networks. Journal of Mathematical Psychology, 44, 536—568.

Wasserman, S., & Pattison, P.E. (2000). Statistical models for social networks. In Kiers, H., Rasson, J.-P., Groenen, P.J.F., and Schader, M. (eds.) Data Analysis, Classification, and Related Methods: Proceedings of the 7th Conference of the International Federation of Classification Societies, pages 285–297. Heildelberg: Springer–Verlag.

Wellman, B., & Wasserman, S. (2000). Social networks. In Kazdin. A. (ed.), Encyclopedia of Psychology, Volume 7, pages 351–353. New York: American Psychological Association and Oxford University Press.

Faber, A., & Wasserman, S. (2001). Social support and social networks: Synthesis and review. In Levy, J., and Pescosolido, B. (eds.) Social Networks and Health. Advances in Medical Sociology, 8, 29–72. Stamford, CT: JAI Press.

Pattison, P.E., & Wasserman, S. (2001). Social network models, statistical. In Smelser, N.J., and Baltes, P.B. (eds.) International Encyclopedia of the Social and Behavioral Sciences, pages 14375–14380. Oxford, UK: Pergamon.

Pattison, P., & Wasserman, S. (2002). Multivariate graph distributions: Applications to social networks. In Hagberg, J. (ed.) Contributions to Social Network Analysis, Information Theory, and Other Topics in Statistics: A Festschrift in Honor of Ove Frank on the Occasion of his 65th Birthday, pages 74–100. Stockholm: University of Stockholm Press.

Wasserman, S., & Pattison, P. (2003). Network analysis. In Lewis-Beck, M., Bryman, A., and Liao, T.F. (eds.) Encyclopedia of Social Science Research Methods. Oregon, OH: Sage Publications.

Wasserman, S. (2003). Multitheoretical, multilevel – and multianalytical: A Forward to Theories of Communication Networks. In Monge, P.R., and Contractor, N., Theories of Communication Networks, pages vii–x. New York: Oxford University Press.

Wasserman, S., & Steinley, D. (2003). Sensitivity Analysis of Network Data and Statistics. National Academy of Sciences Workshop on Dynamic Social Network Modeling and Analysis. Washington, DC: National Academies Press.

Templin, J., Ho, R.M., Anderson, C.J., &Wasserman, S. (2004). Mixed e↵ects p⇤ model for multiple social networks. Proceedings of the American Statistical Association 2003 Annual Meeting, Bayesian Statistics Section. pages 4198-4024. Alexandria, VA: American Statistical Association.

Wasserman, S., & Robins, G. (2004). An introduction to random graphs, dependence graphs, and p⇤. In Carrington, P.J., Scott, J., and Wasserman, S. (eds.), Models and Methods in Social Network Analysis. New York: Cambridge University Press.

Wasserman, S., Scott, J., & Carrington, P.J. (2004). Introduction. In Carrington, P.J., Scott, J., and Wasserman, S. (eds.), Models and Methods in Social Network Analysis. New York: Cambridge University Press.

Wasserman, S., Pattison, P., & Steinley, D. (2005). Social network analysis. In Everett, B. and Howell, D. (eds.) Encyclopedia of Statistics in the Behavioral Science, pages 1866–1871. New York: Wiley.

Contractor, N., Wasserman, S., & Faust, K. (2006). Testing multi-theoretical multilevel hypotheses about organizational networks: An analytic framework and empirical example. Academy of Management Review, 31, 681–703.

Espelage, D.L., Wasserman, S., & Fleisher, M. (2006). Social networks and violent behavior. In Flannery, D.J., Vazsonyi, A., and Waldman, I. (eds). Cambridge Handbook of Violent Behavior. New York: Cambridge University Press.

Parkhe, A., Wasserman, S., & Ralston, D. (2006). New frontiers in network theory development. Academy of Management Review, 31, 560–568.

Wasserman, S. (2007). Discussion of “Model-based clustering for social networks” by Handcock, M.S., Raftery, A., and Tantrum, J. Journal of the Royal Statistical Society, Series B (Invited Paper), 170, 345.

Wasserman, S., Robins, G., & Steinley, D. (2007). Statistical models for networks: A brief review of recent research. In Airoldi, E., et al, (editors) Statistical Network Analysis: Models, Issues, and New Directions. ICML 2006 Workshop. Berlin: Springer Lecture Note Series in Computer Science, #4503, pages 45–56. Also Technical Report #06-01, Indiana University Department of Statistics Technical Report Series.

Steinley, D., & Wasserman, S. (2007). Approximate distributions of several common graph statistics: Hypothesis testing applied to a terrorist network. 2006 Proceedings of the American Statistical Association, Statistical Applications in Defense and National Security. Santa Monica, CA: Rand Corporation. Also Technical Report #06-03, Indiana University Department of Statistics Technical Report Series.

Espelage, D.L., Green, H.D., & Wasserman, S. (2008). Statistical analysis of friendship patterns and bullying behaviors among youth. New Directions in Child and Adolescent Development: Social Network Analysis and Children’s Peer Relationships, Number 118, pages 61–75. Also Technical Report #08-02, Indiana University Department of Statistics Technical Report Series.

Wasserman, S. (2010). Mr. Holland’s networks: A brief review of the importance of statistical studies of local subgraphs or One small tune in a large opus. In Dorans, N.J., & Sinharay, S. (eds.) Looking Back: Proceedings of a Conference in Honor of Paul Holland. New York: Springer. Also Technical Report #08-08, Indiana University Department of Statistics Technical Report Series. December, 2008.

Steinley, D., Brusco, M., & Wasserman, S. (2011) Clusterwise p⇤ models for social network analysis. Statistical Analysis and Data Mining. 4, 487–496.

Steinley, D., & Wasserman, S. (2011). Introduction: Special issue of Statistical Analysis and Data Mining on Networks. Statistical Analysis and Data Mining. 4, 459–460.

Green, H., & Wasserman, S. (2012). Network analysis: A definitional guide to important concepts. In Little, T.D. (ed.) Oxford Handbook of Quantitative Methods. New York: Oxford University Press.

Wasserman, S., & Robins, G. (2012). Social network research: The foundation of network science. Handbook of Research Methods in Psychology. New York: APA Press.

Wasserman, S. (2013). Comment on “Social contagion theory: Examining dynamic social networks and human behavior” by Nicholas Christakis and James Fowler. Statistics in Medicine. 32, 578–580. Also Technical Report #12-01, Indiana University Department of Statistics Technical Report Series.

Brandes, U., Robins, G., McCranie, A., & Wasserman, S. (2013). Editorial: What is network science? Network Science. 1, 1–15.

McCranie, A., & Wasserman, S. (2013). Editorial: What we publish. Network Science. 1, to appear..

Ehrlich, K., Muller, M., Matthews, T., Ross, S., Martin, M., Weng, C., & Wasserman, S. (2013). Statistical analysis of online networks of managers.


Wasserman, S., & Faust, K. (1994). Social Network Analysis: Methods and Applications. New York and Cambridge, ENG: Cambridge University Press.

Wasserman, S., & Galaskiewicz, J. (1994) (eds). Advances in Social Network Analysis: Research from the Social and Behavioral Sciences. Newbury Park, CA: Sage Publications.

Carrington, P., Scott, J., &Wasserman, S. (2004) (eds). Models and Methods in Social Network Analysis. New York: Cambridge University Press. (Winner, 2006 Harrison White Outstanding Book Award from the Mathematical Sociology Section of the American Sociological Association)

Faust, K., & Wasserman, S. (in preparation). Social Network Analysis: Methods and Applications, Second Edition. New York and Cambridge, ENG: Cambridge University Press.


PhD: Harvard University, 1977
Master of Arts: Harvard University, 1974

Master of Arts: University of Pennsylvania, 1973

‘The Future Belongs to Network Analysis’

What do staff efficiency, power of the Medici family, and the Ebola epidemic have in common? It is that they can be studied with network analysis. In 2017, HSE launched a new English-taught master’s programme ‘Applied Statistics with Network Analysis’.Valentina Kuskova, head of theInternational laboratory for Applied Network Research, told the HSE news service how network research works in social studies.

Network Analysis: Methods for Solving Real-life Problems

From 24th to 28th July, Moscow hosted the Eighth International summer school 'Theory and Methods of Network Analysis' for students and researchers, held by the HSE International Laboratory for Applied Network Research. Its academic supervisor Stanley Wasserman from Indiana University Bloomington took part in the summer school's work.

ANR-Lab`s Summer School on the foundations of network analysis

On July 24-28, 2017, International Laboratory for Applied Network Research has held the Eighth International Summer School «Theory and Methods of Network Analysis» (TMSA(II)-2017) on the foundations of network analysis.

“Applied Network Analysis: Models and Applications” at the XVII April International Academic Conference 

On April 20 within the XVII April International Academic Conference on Economic and Social Development the Laboratory for Applied Network Research has held a section “Applied Network Analysis: Models and Applications”.

Research at HSE: the Power of Collaboration

The HSE Look has begun a series of articles on research life at HSE. The new issue that came out today focuses on cooperative efforts in research and gives several detailed examples of joint work at our university. We will start with the International Laboratory for Applied Network Research who share their experience of starting up a research unit and successfully collaborating with colleagues and students.

Contemporary Management: Developing Research and Researchers

Three events in one at the HSE Faculty of Management combined an academic seminar of young management researchers, the seventh annual research conference ‘Contemporary management problems: exploring the boundaries’ and the first international conference on network analysis. The speakers and audience were the same for all three. 

Prof. Wasserman will participate as key speaker in workshop  "Multivariate Techniques for the Analysis of Network Data"

Prof. Wasserman will participate as key speaker in workshop  "Multivariate Techniques for the Analysis of Network Data", Solerno, Italy.

Russian Network Analysis Begins at the HSE

The HSE has set up a new International Laboratory for Applied Network Analysis. The lab’s Academic Supervisor, Professor at Indiana University, Stanley S. Wasserman and Deputy Dean for International Relations at the HSE Faculty of Management, Valentina Kuskova talked to the News Service about the aims of the new laboratory.