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Date
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Topic
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Reading
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Homework (DATES ONLY APPROXIMATE)
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Thursday
September 25
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Introduction
Social science is the study of relationships and relationships can be
represented via social networks
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Borgatti: Introduction to Social Networks
Radcliffe-Brown(1940): On Social Structure
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Thursday
October 2
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Graph Theory and Notation
Nodes, ties (directed/undirected), degree, connectedness, cycles,
centrality, betweenness, etc
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W&F Chapter 3
H&R Chapter 3
Borgatti (1994) (suggested)
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Tuesday
October 7
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Data structures for representing graphs
Sociomatrix, edge list, R network data types, datasets, (bipartite,
affiliation)
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W&F Chapter 4
H&R Chapter 4
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Homework 1
Find paper on networks, read and summarize
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Thursday
October 9
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Introduction to R
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All about R notes
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Tuesday
October 14
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Introduction to Networks in R
sna and network packages
reading and manipulating data, plotting, computing descriptive statistics
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Sunbelt handout
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Homework 2
Doing network descriptives by hand
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Thursday
October 16
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Stochastic Models of Networks (estimation and
inference)
Model 1: Reney-Erdos: 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)
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W&F Chapter 13.1-13.5
Snijders
(2003)
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Tuesday
September 30
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Motivation
Overview of the use of social networks to model social structure important for understanding the spread of HIV.
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Local Acts, Global Consequences: Networks and the Spread of HIV
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Tuesday
October 21
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Modeling Cohesive Subgroups
arbitrary mixing groups known a priori
likelihood inference
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W&F Chapter 7
H&R Chapter 7
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Homework 3
Use R (sna, network) to read data, descriptives, plots, centrality, etc.
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Thursday
October 23
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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
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Nowicki, K. & Snijders(2001). Estimation and prediction for stochastic
block models. Journal of the American Statistical Association, 96,
1077-1087
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Tuesday
October 28
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Models for Fundamental Social Forces
1. Centrality (degree centrality, eigenvalue
centrality)
2. Sociality (undirected)
3. Prestige (directed)
4. Mutuality (directed)
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W&F Chapter 5
H&R Chapter 6
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Homework 4
Example Reney-Erdos, vertex attributes, mixing
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Thursday
October 30
&
Tuesday
November 4
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Modeling Cohesive Groups in Social Space
Network position (latent social space, probability of a tie proportional to
distance)
1-dimensional continuous observed
2-dimensional continuous observed
2-dimensional continuous unobserved
latent space cluster models
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Hoff, Raftery, & Handcock (2001)
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Homework 5
a) Latent class model
b) centrality model
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Thursday
November 6
&
Tuesday
November 11
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Introduction to general ERGM framework
general form
conditional independence models: Markov models, Hammersley-Clifford
1. Simulation of network via MCMC
2. Likelihood-based inference
3. Maximum likelihood and Bayesian inference
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Hunter (2003)
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Homework 6
Latent space models
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Thursday
November 13
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Structure of triads: Triad Census
(Davis-Holland-Leinhardt)
transitivity
balance model (Heider)
Simmel model
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W&F Chapter 14
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Tuesday
November 18
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Mores sophisticated structural forms
cycles, triangles, gwsp, dsp,
esp, stars
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Snijders et al (2006)
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Homework 7
ERGM theory and MCMC, simulation of graphs
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Thursday
November 20
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Goodness of fit of ERGMs
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Hunter, Goodreau, & Handcock
(2006)
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Tuesday
November 25
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Inference for partially observed networks
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Handcock and Gile (2007), Gile and Handcock (2008)
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Thursday
December 2
&
Tuesday
December 4
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Sampling of networks (design)
ego-centered, link tracing
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Gile and Handcock (2008); Frank (2004) Chapter 4
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Homework 8
Triad census
Heider vs. Simmel
More sophisticated models
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Extra
December
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Network Dynamics
Summary
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Homework 9
Goodness of fit sampling examples
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