Finalizing tom's section

Author: gvegayon

figs/slides-double-files.png

@@ -11,9 +11,9 @@ knitr::opts_chunk$set(comment = "#")
 ```
 
 
-Workshop materials can be downloaded from https://github.com/USCCANA/netdiffuser-sunbelt2018/tree/sunbelt2019. You can download the entire repository (source code of the workshop) [here](https://github.com/USCCANA/netdiffuser-sunbelt2018/archive/sunbelt2019.zip).
+Workshop materials can be downloaded from <https://github.com/USCCANA/netdiffuser-workshop>.
 
-Source code per section:
+Sections:
 
 1. [Getting started](getstarted.qmd) (optional)
 2. [Diffusion Theory](theory.qmd)

figs/slides-file-long.png

@@ -269,55 +269,84 @@ they first used a modern family planning method.
 | In Degree | 1.04 | 1.06\*\* | 1.02\* |
 | Exposure (Cohesion) | 0.94 | 1.16 | 2.16\*\* |
 
+## Simulating diffusion
+
+- Seeds:
+    - Type (central, bridging, marginal, random, etc.)
+    - Size (e.g., 5%, 10%, 15%)
+
+- Network structure:
+    - Random
+    - Small world
+    - Empirical
+    - Scale-free (centralized)
+    - Attribute-based (e.g., homophily)
+
+- Threshold distribution:
+    - Uniform
+    - Normal (varying variance)
+    - Skewed 
+    - Beta
+
+- Mechanism:
+    - Cohesion
+    - Structural equivalence
+    - Indirect ties
+    - Attribute-based (e.g., homophilous more persuasive)
+
+- Adoption behavior:
+    - No-disadoption
+    - Disadoption
+    - Conflicting behaviors (perhaps coded as -1,0, 1)
+    - Incorporate uncertainty
+
+## Reading Data Challenges
 
+- Merging attribute and network data
 
+    - Nominated but no attribute data
 
+    - Attribute data no network data
 
+- Data file formats
 
+    - Single flat file
 
+    - 2 files: edgelist and attribute file (flat)
 
+    - Cohort studies: 1 file separate waves of data
 
+## Formats
 
+- Survey Data (static & dynamic)
 
+- Edge-list and Attribute data
 
+- Panel Data
 
+- STATNET, Igraph, ....
 
+## Input Types: Single Flat File
 
+- Flat File with IDs, Nodelist, and Time of Adoption
+- Typically a retrospective study
+- Examples, Medical Innovation, Korean Family Planning; Brazilian Farmers
 
+![](figs/slides-single-flat.png){width="40%"}
 
+## Input Types: Double Files
 
+- Files will have mostly matching IDs 
+- One or both files may contain time information
+- Edgelist may also, potentially, contain values or spell information
 
+![](figs/slides-double-files.png){width="80%"}
 
-Simulating Diffusion
-
-Reading Data Challenges
-
-- Merging attribute and network data
-
-- Nominated but no attribute data
-
-- Attribute data no network data
-
-- Data file formats
-
-- Single flat file
-
-- 2 files: edgelist and attribute file (flat)
-
-- Cohort studies: 1 file separate waves of data
-
-Formats
-
-- Survey Data (static & dynamic)
-
-- Edge-list and Attribute data
-
-- Panel Data
-
-- STATNET, Igraph, ....
 
-Input Types: Single Flat File
+## Input Types: Classic Cohort/Longitudinal
 
-Input Types: Double Files
+- IDs and Nodelist Network data 
+- Behavior is typically binary (e.g., ever smoked) or potentially valued (e.g., # of cigarettes
+- Examples SNS
 
-Input Types: Classic Cohort/Longitudinal
+![](figs/slides-file-long.png){width="50%"}