Introduction to
Stochastic Actor-oriented Models
Types of dataSkills Assessment
András Vörös
Department of Social Statistics, University of Manchester
SOST71032 Social Network Analysis
* Thanks to Christoph Stadtfeld for many nice figures and examples.
Summary: Stochastic Actor-oriented Models
 At certain points in time, actors consider changing the
set of their outgoing network ties
 Each possible choice is described by an objective
function that takes different effects into account
 outdegree (number of ties)
 reciprocity
 transitivity
 homophily
 …
 The probability to change a specific tie is modeled by a
multinomial choice probability model which is based
on the comparison of the objective functions
 Add a tie? Drop a tie? Do nothing?
 How attractive is each outcome relative to the others?
What kind of data can we analyze in RSiena?
 Dependent networks:
 binary (0-1) values (e.g. friendship, alliance)
 directed, undirected, one- or two-mode
 at least two observations
 Dependent “behavior”
 categorical, with few (~2-5 or a bit more) values (attitudes,
behaviors) OR continuous (recent extension)
 at least two observations
 Individual covariates:
 categorical (ethnicity), continuous (salary)
 constant or varying (one or more observations necessary)
 Dyadic covariates:
 basically – explanatory networks
 categorical (co-membership), continuous (distance of homes)
 constant or varying (one or more observations necessary)
How to apply SAOMs in practice?
Please continue with the next topic.