part2a.qmd

---
title: "Part 2a: Social Network Analysis"
---

## Network Diffusion

epiworldR also supports diffusion networks to simulate the spread of ideas or rumors throughout a population. The below example demonstrates this capability.

#### Example

Goal: Create a diffusion network model that simulates a rumor's spread throughout the `UKfaculty` network of size n = 81. We start by loading the corresponding R packages and data.

```{r, include = FALSE}
library(igraph)  # For manipulating networks
library(netplot) # For plotting
```

We now load the `UKfaculty` data and visualize the network. The file `part2a.rda` has the network, the vertex attributes as a numeric matrix, and the edge list as a numeric matrix.

```{r}
# Loading the data for the section
load("part2a.rda")

# Visualizing the Network
# library(netplot)
nplot(UKfaculty)

# First few lines of the vertex attributes
head(UKfaculty_vertex)

# First few lines of the edge list
head(UKfaculty_edgelist)
```

To simulate the rumor, we use the `ModelDiffNet` function:

```{r}
# Creating the diffusion model
library(epiworldR)
adopt_rumor <- ModelDiffNet(
  name       = "The dean is leaving!",
  prevalence = 2/vcount(UKfaculty), # Two adopter
  prob_adopt = .1,
  data       = UKfaculty_vertex,
  params     = c(0, 5)
)

# Reading in the network
agents_from_edgelist(
  adopt_rumor,
  size     = vcount(UKfaculty),
  source   = UKfaculty_edgelist[,1] - 1L,
  target   = UKfaculty_edgelist[,2] - 1L,
  directed = TRUE
  )

# Running the model for 50 steps
run(adopt_rumor, 100);adopt_rumor

# Plotting the model
plot(adopt_rumor)
```

We can extract the adoption network:


```{r}
transmissions <- get_transmissions(adopt_rumor)

# Turning it into a network via igraph
tnet <- graph_from_data_frame(
  transmissions[, c(2, 3)] + 1, # To start from 1 (not 0)
  directed = TRUE,
  vertices = cbind(id = 1:81, UKfaculty_vertex)
  )

# Adding back vertex attributes
nplot(tnet)
```