Date

Topic

Reading

Homework (DATES ONLY APPROXIMATE)

Thursday
September 25

Introduction
Social science is the study of relationships and relationships can be
represented via social networks

Borgatti: Introduction to Social Networks
RadcliffeBrown(1940): On Social Structure


Thursday
October 2

Graph Theory and Notation
Nodes, ties (directed/undirected), degree, connectedness, cycles,
centrality, betweenness, etc

W&F Chapter 3
H&R Chapter 3
Borgatti (1994) (suggested)


Tuesday
October 7

Data structures for representing graphs
Sociomatrix, edge list, R network data types, datasets, (bipartite,
affiliation)

W&F Chapter 4
H&R Chapter 4

Homework 1
Find paper on networks, read and summarize

Thursday
October 9

Introduction to R

All about R notes


Tuesday
October 14

Introduction to Networks in R
sna and network packages
reading and manipulating data, plotting, computing descriptive statistics

Sunbelt handout

Homework 2
Doing network descriptives by hand

Thursday
October 16

Stochastic Models of Networks (estimation and
inference)
Model 1: ReneyErdos: p(tie) is constant,
independent: joint distribution model, logistic model
Model 2: 2 different types of nodes with different probabilities of ties
Model 3: Vertex covariates model (logistic regression)

W&F Chapter 13.113.5
Snijders
(2003)


Tuesday
September 30

Motivation
Overview of the use of social networks to model social structure important for understanding the spread of HIV.

Local Acts, Global Consequences: Networks and the Spread of HIV


Tuesday
October 21

Modeling Cohesive Subgroups
arbitrary mixing groups known a priori
likelihood inference

W&F Chapter 7
H&R Chapter 7

Homework 3
Use R (sna, network) to read data, descriptives, plots, centrality, etc.

Thursday
October 23

Modeling Cohesive Subgroups continued
multiple groups unknown (latent class model)
Model 2: 2 different types of nodes with different probabilities of ties
Inference for models

Nowicki, K. & Snijders(2001). Estimation and prediction for stochastic
block models. Journal of the American Statistical Association, 96,
10771087


Tuesday
October 28

Models for Fundamental Social Forces
1. Centrality (degree centrality, eigenvalue
centrality)
2. Sociality (undirected)
3. Prestige (directed)
4. Mutuality (directed)

W&F Chapter 5
H&R Chapter 6

Homework 4
Example ReneyErdos, vertex attributes, mixing

Thursday
October 30
&
Tuesday
November 4

Modeling Cohesive Groups in Social Space
Network position (latent social space, probability of a tie proportional to
distance)
1dimensional continuous observed
2dimensional continuous observed
2dimensional continuous unobserved
latent space cluster models

Hoff, Raftery, & Handcock (2001)

Homework 5
a) Latent class model
b) centrality model

Thursday
November 6
&
Tuesday
November 11

Introduction to general ERGM framework
general form
conditional independence models: Markov models, HammersleyClifford
1. Simulation of network via MCMC
2. Likelihoodbased inference
3. Maximum likelihood and Bayesian inference

Hunter (2003)

Homework 6
Latent space models

Thursday
November 13

Structure of triads: Triad Census
(DavisHollandLeinhardt)
transitivity
balance model (Heider)
Simmel model

W&F Chapter 14


Tuesday
November 18

Mores sophisticated structural forms
cycles, triangles, gwsp, dsp,
esp, stars

Snijders et al (2006)

Homework 7
ERGM theory and MCMC, simulation of graphs

Thursday
November 20

Goodness of fit of ERGMs

Hunter, Goodreau, & Handcock
(2006)


Tuesday
November 25

Inference for partially observed networks

Handcock and Gile (2007), Gile and Handcock (2008)


Thursday
December 2
&
Tuesday
December 4

Sampling of networks (design)
egocentered, link tracing

Gile and Handcock (2008); Frank (2004) Chapter 4

Homework 8
Triad census
Heider vs. Simmel
More sophisticated models

Extra
December

Network Dynamics
Summary


Homework 9
Goodness of fit sampling examples
