COM 411

```{r setup, include=FALSE} knitr::opts_chunk$set(echo = FALSE) knitr::opts_knit$set(root.dir = './') source("resources/preamble.R") f <- function (x) {formatC(x, format="d", big.mark=',')} bold <- function(x) {paste('{\\textbf{',x,'}}', sep ='')} gray <- function(x) {paste('{\\textcolor{gray}{',x,'}}', sep ='')} wrapify <- function (x) {paste("{", x, "}", sep="")} p <- function (x) {formatC(x, format='f', digits=1, big.mark=',')} library(igraph) ```

COM 41100: Communication and Social Networks

Tuesday and Thursday, 3:00-4:15

# Welcome! ## Agenda for the day > - Introductions > - About the class ## About me
## Introductions - Name - Year - Major - Something boring about you ## What is Communication and Social Networks?
NOT THIS KIND OF SOCIAL NETWORK!
## Social networks are much more interesting! >- How do groups make decisions >- How do fads and fashions start and spread? >- How do our connections influence our perceptions about and behavior in the world? ## Goals > 1. Understand the foundations of network theory and analysis > 2. Critically read social network studies > 3. Learn how social networks relate to your own interests > 4. Gain a basic understanding of gathering and analyzing network data in R # How we reach those goals ## Assignments >- Homework > - Social network concepts > - Programming practice >- Reading >- Participation (in-class and online) ## Textbook >- Multiple readings from Six Degrees: The Science of a Connected Age (~$10) ## Exams >- Two in-class exams ## Final Project >- Group-based, network-inspired project ## Grading >- Normal grading has some negative unintended consequences
>- How can we build a learning community? ## Grading >- I'm interested in teaching, not assessing >- Assignments will be turned in on Brightspace >- I will provide feedback >- 4 times during the semester you will turn in reflection pieces >- If I disagree I will reach out ## Dangers of this approach to grading ## Programming >- You will be able to do stuff like this:
```{r, echo = T, message = F, fig.height = 5} z <- graph("Zachary") # the Zachary karate club ceb <- cluster_edge_betweenness(z) # Identify communities V(z)$community = ceb$membership # Add communities as node attributes plot(z, vertex.color = V(z)$community, # Color by community vertex.size = sqrt(betweenness(z)), # Size by betweenness centrality vertex.label = NA) # Remove labels ```
## Getting in touch >- Office Hours >- Email ## Tech in the classroom ## Please be vocal >- I will solicit feedback as part of reflections >- Let me know what is and isn't working ## Assignments >- Read the syllabus >- Fill out the survey (linked on syllabus) >- Read preface and chapter 1 of Six Degrees > - And order the book >- Introduce yourself (and give thoughts on the readings?) on Brightspace # Simulation Activity ## Read Instruction Sheet - Only communicate through written messages - No talking - Write your number and time finished on the board ## How did it go? >- What worked well? >- What didn't? >- Who was fastest? Why? >- What was the longest path a resource traveled? ## Global network ## Debrief topics to cover - Reciprocity - Degree centrality - Social capital - Structural holes - Reputation and deceit - Position and power - Multiplexity - Number vs. type of ties - Global/local networks - Tie maintenance - Sponsorship - Norms and social conventions ## Meta discussion - How does this differ from real-world networks? - What does this make you see differently?