Code-R.Rmd

---
title: "Code-R"
author: "Tianchen Gao"
output: html_document
---

## Introduction

This code uses a citation network as an example to demonstrate network construction, basic descriptive analysis, and visualization.

```{r,warning=FALSE,message=FALSE}
# Load the required packages
library(igraph)
```

## Construct Citation Network

```{r}
# Read in the edge data (citation network)
citation <- read.csv("Edgelist_citation_network.csv")
# Filter by year (2000-2005)
citation <- citation[which(citation$Year <= 2005 & citation$Year >= 2000), ]
# Construct the citation network
g <- graph_from_data_frame(citation[,c("Source","Target")], directed = TRUE)
# View the network
g
```

## Descriptive Analysis

### Basic

```{r,fig.align='center',fig.height=6,fig.width=6}
# Number of nodes and edges in the network
c(vcount(g), ecount(g))
# Network density
edge_density(g)
# Node degree
d <- degree(g)
# Minimum and maximum degree values
c(min(d), max(d))
```

### Degree Distribution 

```{r,fig.align='center',fig.height=5,fig.width=12}
# Set up the canvas
par(mfrow = c(1, 2))
# Plot degree distribution histogram
hist(degree(g), xlab = "Degree", ylab = "Frequency", main = "")
# Plot double-log degree distribution graph
# Count
data_freq <- data.frame(table(degree(g)))
data_freq$Var1 <- as.numeric(as.character(data_freq$Var1))
# Plot
plot(log(as.numeric(data_freq$Var1)), log(data_freq$Freq), xlab = "Log-Degree", ylab = "Log-Count")
```

### Dyadic Structures

```{r}
#Calculate the counts of three types of dyadic structures using the dyad.census() function
dyad.census(g)
```

### Centrality

```{r,warning=FALSE}
# Degree centrality
head(degree(g, normalized = T))
# Closeness centrality
head(closeness(g, normalized = T))
# Betweenness centrality
head(betweenness(g, normalized = T))
```

### Neighbors

```{r}
n <- neighbors(g, 'Paper_4')
print(n)
```

### Ego Network

```{r}
# Extract node neighborhoods
gn = graph.neighborhood(g, order = 1)
# Display the number of nodes in the subgraph of the first 20 node neighborhoods
sapply(gn, vcount)[1:20]
```

## Visualization

```{r,fig.align='center',fig.width=5,fig.height=5}
# Set seed
set.seed(42)
#Plot the 1st order neighborhood subgraph of node "Paper_4"
plot(gn[[which(V(g)$name == "Paper_4")]],
  # Set node size
  vertex.size = 10,
  # Set node color
  vertex.color = 'lightsteelblue2',
  # Set node border color
  vertex.frame.color = 'skyblue4',
  # Set vertex label size
  vertex.label.cex = 0.8,
  # Set distance between node and label to avoid overlap
  vertex.label.dist = 2,
  # Fruchterman-Reingold layout
  layout = layout.fruchterman.reingold)
```