Social Networks: Statistical Approaches

A 2-Day Seminar Taught by John Skvoretz, Ph.D.

To see a sample of the course materials, click here.

The study of social networks focuses on relationships among the units of some population, and on how the structure of these ties affects outcomes experienced by both the units and the population. Often the units are persons or individuals, but they may be families, households, corporations, or nation states.

Social network analysis is a set of methods for describing, quantifying and analyzing the properties of social networks. This seminar is survey of statistical methods for analyzing social network data. It will focus on testing hypotheses about:

  • network structure (e.g. reciprocity, transitivity, centralization),
  • the formation/dissolution of ties based on attributes (e.g. homophily),
  • local structural effects.

The course begins with models for the local structure of dyads and triads, and next moves to models based on the assumption of dyadic independence. We will then consider models that permit structured forms of dependence between dyads.

Topics include statistical models for local structure (dyads and triads) and graph-level indices, biased net models for complete networks and for aggregated tie count data, exponential random graph models, and stochastic actor-oriented models. Each morning and afternoon will be divided into a presentation of methods and a lab using those methods. This workshop assumes that participants have already taken a first course in network analysis, or have acquired equivalent knowledge through self study.


All software uses the R environment as implemented in RStudio or directly in the standard console interface. Analysis relies on functions and packages developed for that environment including custom functions developed by the instructor and the packages: network, sna, statnet, and RSiena.

If you’d like to take this course but are concerned that you don’t know enough R, there are excellent on-line resources for learning the basics. Here are our recommendations.

Who should attend

This workshop does assume that participants have already taken a first course in network analysis (or have acquired equivalent knowledge through self study). Participants should be familiar with such network concepts as density, reciprocity, centrality and centralization, and clustering coefficients.


The class will meet from 9 am to 5 pm each day with a 1-hour lunch break at Temple University Center City, 1515 Market Street, Philadelphia, PA 19103.

Participants receive a bound manual containing detailed lecture notes (with equations and graphics), examples of computer printout, and many other useful features. This book frees participants from the distracting task of note taking.

Registration and lodging

The fee of $995.00 includes all seminar materials.

Refund Policy

If you cancel your registration at least two weeks before the course is scheduled to begin, you are entitled to a full refund (minus a processing fee of $50). 

Lodging Reservation Instructions

A block of guest rooms has been reserved at the Club Quarters Hotel, 1628 Chestnut Street, Philadelphia, PA at a special rate of $177 per night. This location is about a 5-minute walk to the seminar location. In order to make reservations, call 203-905-2100 during business hours and identify yourself by using group code SH1114 or click here. For guaranteed rate and availability, you must reserve your room no later than Monday, October 14, 2019.

If you need to make reservations after the cut-off date, you may call Club Quarters directly and ask for the “Statistical Horizons” rate (do not use the code or mention a room block) and they will try to accommodate your request.


1.  Analysis of hypotheses about reciprocity, multiplexity, exchange, transitivity, density, degree, centralization, clustering coefficient and other graph‐level indices
          a.  Testing for effects like reciprocity, multiplexity, and exchange in dyads
          b.  Testing for effects like transitivity and closure in triads
          c.  Evaluating hypotheses about graph-level indices like density, mean
               degree, centralization scores, clustering coefficients against
               chance expectations
2.  Biased net models for aggregate tie counts and complete networks
          a.  Attraction and repulsion mechanisms for homophily
          b.  Unidimensional and multidimensional analysis of intergroup ties
          c.  Complete network models: forms of dyadic dependence in models for
               cross-sectional data and in models for longitudinal data
3.  Exponential random graph models (ergm), their specification and estimation with statenet
          a.  Global and local form of an ergm
          b.  Model specification and estimation in statnet
          c.  Families of ergms from Bernoulli to social circuit models
          d.  Ergms for two-mode network data, for valued network data, and for
               longitudinal network data
4.  Stochastic actor‐oriented models (saoms) and RSiena
          a.  Modeling the rate function, the evaluation function, and the
               behavior function
          b.  Model specification and estimation in RSiena
          c.  Common effects in saoms for ties and behavior