How to Implement Social Network Analysis in ns3

To implement the social network analysis (SNA) in ns3 has several steps. We need to use ns3 network simulator for modelling and simulation of network protocols and systems.

The given below is the sample approach to implement the SNA in ns3 tool:

Step-by-step Implementation:

Step 1: Understanding Social Network Analysis

Before diving into ns3, we need to understand what social network analysis needs. SNA includes to analyse the structure of relationships among social entities (nodes) using graph theory. Key metrics contains:

• Degree centrality: Number of connections a node has.
• Betweenness centrality: Number of times a node acts as a bridge along the shortest path between two other nodes.
• Closeness centrality: Average shortest path length from a node to all other nodes.
• Network density: Ratio of the number of edges to the number of possible edges.

Step 2: Setting Up ns3 Environment

If you haven’t already, set up your ns3 environment:

1. Download ns3: Install ns3.
2. Install ns3: Follow the installation instructions for your operating system.
3. Familiarize with ns3 basics: Understand how to create nodes, set up channels, and run basic simulations.

Step 3: Modeling the Social Network

1. Define the Network Topology:
   • To represents the social network makes the network topology. we use graph data structures to represent nodes and edges.
   • Example: You can use Python’s NetworkX library to generate social network topologies and export them for ns-3.
2. Translate the Topology to ns3:
   • In ns3, create nodes and set up links based on the social network topology.
   • Example code snippet to create nodes and links:

#include “ns3/core-module.h”

#include “ns3/network-module.h”

#include “ns3/internet-module.h”

#include “ns3/point-to-point-module.h”

using namespace ns3;

int main(int argc, char *argv[])

{

NodeContainer nodes;

nodes.Create(5); // Create 5 nodes representing 5 social entities

PointToPointHelper pointToPoint;

pointToPoint.SetDeviceAttribute(“DataRate”, StringValue(“5Mbps”));

pointToPoint.SetChannelAttribute(“Delay”, StringValue(“2ms”));

NetDeviceContainer devices;

for (uint32_t i = 0; i < nodes.GetN() – 1; ++i)

{

for (uint32_t j = i + 1; j < nodes.GetN(); ++j)

{

devices.Add(pointToPoint.Install(nodes.Get(i), nodes.Get(j)));

}

}

InternetStackHelper stack;

stack.Install(nodes);

Ipv4AddressHelper address;

address.SetBase(“10.1.1.0”, “255.255.255.0”);

Ipv4InterfaceContainer interfaces = address.Assign(devices);

Simulator::Run();

Simulator::Destroy();

return 0;

}

Step 4: Simulating Social Network Behaviour

1. Implement Communication Patterns:
   • To simulate social interactions describe communication patterns among nodes. For example, nodes can periodically send messages to their neighbours.
2. Collect Data for Analysis:
   • Use ns3 tracing mechanisms are to gather data on node interactions. For instance, we can make use of packet tracing to log communication events.

Step 5: Analysing the Data

1. Export Simulation Data:
   • After running the simulation, export the collected data (e.g., packet traces) for further analysis.
2. Perform SNA Metrics Calculation:
   • Use SNA tools and libraries like NetworkX in Python is to compute the desired social network metrics from the exported data.

Example Analysis Using Python and NetworkX

import networkx as nx

# Create a graph from ns-3 exported data

G = nx.Graph()

# Add nodes and edges based on ns-3 simulation output

G.add_nodes_from([0, 1, 2, 3, 4])

G.add_edges_from([(0, 1), (0, 2), (1, 2), (2, 3), (3, 4)])

# Compute SNA metrics

degree_centrality = nx.degree_centrality(G)

betweenness_centrality = nx.betweenness_centrality(G)

closeness_centrality = nx.closeness_centrality(G)

density = nx.density(G)

print(“Degree Centrality:”, degree_centrality)

print(“Betweenness Centrality:”, betweenness_centrality)

print(“Closeness Centrality:”, closeness_centrality)

print(“Network Density:”, density)

As we discussed earlier about how the social network analysis will perform in ns3 implementation tool and we help to provide further information about how the social network analysis will adapt in different environments.

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