Plotting Network using igraph

1. Explain the data fields. Choose the specific data fields that you want to use to build the network.

Dataset information This is who-trusts-whom network of people who trade using Bitcoin on a platform called Bitcoin OTC. Since Bitcoin users are anonymous, there is a need to maintain a record of users’ reputation to prevent transactions with fraudulent and risky users. Members of Bitcoin OTC rate other members in a scale of -10 (total distrust) to +10 (total trust) in steps of 1. This is the first explicit weighted signed directed network available for research.

Bitcoin OTC trust weighted signed network

Dataset statistics

Nodes 5,881
Edges 35,592
Range of edge weight -10 to +10
Percentage of positive edges 89%

Fields:

The Bitcoin OTC dataset includes the following fields:

SOURCE: ID of the node that initiated the rating (the rater).
TARGET: ID of the node being rated (the ratee).
RATING: The rating given by the source to the target, ranging from -10 (worst trustworthiness) to +10 (highest trustworthiness).
TIME: Timestamp of the rating, measured in seconds since the Epoch (Unix timestamp).

Fields for Network Construction:

Use SOURCE and TARGET to build the network (nodes and edges).
Use RATING as a weight for the edges to study the strength or polarity of the relationships.

2. Read the network data from csv files and display the data in a table in R.

# Load required libraries
library(igraph)

## 
## Attaching package: 'igraph'

## The following objects are masked from 'package:stats':
## 
##     decompose, spectrum

## The following object is masked from 'package:base':
## 
##     union

# Read CSV file
data <- read.csv("C:/UTM/Semester 2/SOCIAL NETWORK ANALYTICS/Assignment 1 (Part 2)/soc-sign-bitcoinotc.csv", header = FALSE, stringsAsFactors = FALSE)
colnames(data) <- c("SOURCE", "TARGET", "RATING", "TIME")

# Display the first few rows
head(data)

3. Plot the Network using igraph

# Create a graph object
graph <- graph_from_data_frame(d = data[, c("SOURCE", "TARGET")], directed = TRUE)

# Plot the network
plot(graph, vertex.size = 5, vertex.label = NA, edge.arrow.size = 0.5, main = "Bitcoin OTC Network")

4. Examine the data:

a. Find number of nodes

num_nodes <- vcount(graph)
cat("Number of nodes:", num_nodes, "\n")

## Number of nodes: 5881

b. Find number of edges

num_edges <- ecount(graph)
cat("Number of edges:", num_edges, "\n")

## Number of edges: 35592

c. Find the edgelist (“from”, “to”)

edge_list <- as_edgelist(graph)
head(edge_list)

##      [,1] [,2]
## [1,] "6"  "2" 
## [2,] "6"  "5" 
## [3,] "1"  "15"
## [4,] "4"  "3" 
## [5,] "13" "16"
## [6,] "13" "10"

5. Simplify the network by removing all the multiple edges and loops.

# Simplify network: Remove loops and multiple edges
simplified_graph <- simplify(graph, remove.multiple = TRUE, remove.loops = TRUE)

# Plot simplified graph
plot(simplified_graph, vertex.size = 7, edge.arrow.size = 0.5, main = "Simplified Transaction Network")

cat("Number of nodes:", vcount(simplified_graph), "\n")

## Number of nodes: 5881

cat("Number of edges:", ecount(simplified_graph), "\n")

## Number of edges: 35592

R Notebook