---
title: "Visualization"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Visualization}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

```{r setup, message = FALSE}
library(nett)
library(igraph)
```

In this article, we go through some of the basic visualization functionality in the `nett` package. 

## Visualizing a DCSBM

Let us sample a network from a DCSBM: 
```{r }
n = 1500
Ktru = 4
lambda = 15 # expected average degree
oir = 0.1
pri = 1:Ktru

set.seed(1234)
theta <- EnvStats::rpareto(n, 2/3, 3)
B = pp_conn(n, oir, lambda, pri=pri, theta)$B
z = sample(Ktru, n, replace=T, prob=pri)

# sample the adjacency matrix
A = sample_dcsbm(z, B, theta)
```

We can plot the network using community labels $z$ to color the nodes:
```{r}
original = par("mar")

gr = igraph::graph_from_adjacency_matrix(A, "undirected") # convert to igraph object 
par(mar = c(0,0,0,0))
out = nett::plot_net(gr, community = z)

par(mar = original)
```

We can also plot the degree distribution:
```{r}
nett::plot_deg_dist(gr)
summary(igraph::degree(out$gr))
```
## A latent variable model
Now consider a latent variable model with $K$ communities as follows: The adjacency matrix $A = (A_{ij})$ is generated as a symmetric matrix, with independent Bernoulli entries above the diagonal with
\begin{align}\label{eq:dclvm:def}
	\mathbb E [\,A_{ij} \mid x, \theta\,] \; \propto \; \theta_i \theta_j e^{- \|x_i - x_j\|^2} \quad  \text{and}         \quad
	x_i = 2 e_{z_i} + \frac34 w_i
\end{align}
where $e_k$ is the $k$th basis vector of $\mathbb R^d$, $w_i \sim N(0, I_d)$, $\{z_i\} \subset [K]^n$ are multinomial labels (similar to the DCSBM labels) and $d = K$. The proportionality constant in~\eqref{eq:dclvm:def} is chosen such that the overall network has expected average degree $\lambda$

We can generate from this model using the `nett::sample_dclvm()` function as follows:
```{r}
d = Ktru
labels = sample(Ktru, n, replace = T, prob = pri)
labels = sort(labels)
mu = diag(Ktru)
x = 2*mu[labels, ] + 0.75*matrix(rnorm(n*d), n)

A = sample_dclvm(x, lambda, theta)
```

Visualizing the network and its degree distribution goes as before:
```{r}
original = par("mar")

gr = igraph::graph_from_adjacency_matrix(A, "undirected") # convert to igraph object 
par(mar = c(0,0,0,0))
out = nett::plot_net(gr, community = labels)

par(mar = original)
```

```{r}
nett::plot_deg_dist(gr)
summary(igraph::degree(out$gr))
```


## Visualizing *Political Blogs* network
Let us compare with *Political Blogs* network accessible via `polblogs`.

```{r}
original = par("mar")

par(mar = c(0,0,0,0))
out = nett::plot_net(polblogs, community = igraph::V(polblogs)$community)

par(mar = original)
```

```{r}
nett::plot_deg_dist(polblogs)
summary(igraph::degree(polblogs))
```