How to Implement Social Network Analysis in NS2

To implement social network analysis (SNA) in ns2 has usually accomplished on social communication networks to evaluate relationships among individuals, groups, or entities. In NS2, that is mainly intended for mimic the networking protocols, SNA can be executed by modelling nodes by way of social entities (users, devices, or individuals) and mimicking their communication, relationships, and interaction behaviours. Since NS2 is not characteristically intended for SNA, that can emulate social networks by considering nodes as representing users and their interactions as communication flows or events, that are evaluated using SNA approaches. The given below is the step by procedure to implement the social network analysis in ns2:

Key Concepts for Simulating Social Network Analysis in NS2:

  1. Node Interaction: Nodes denotes individuals, devices, or social entities, and communicates among nodes are simulated using communication flows.
  2. Relationships: Model relationships among nodes according to communication patterns, like frequency or duration of interaction.
  3. Network Metrics: Capture social network parameters like degree centrality, closeness centrality, and betweenness centrality based on node interactions.
  4. Analysis of Interaction Patterns: Use communication characteristics to develop insights into social network structures, group dynamics, or influential nodes.

Steps to Implement Social Network Analysis in NS2

  1. Define Social Entities as Nodes: Each node in the NS2 simulation denotes a social entity like a user, device, or individual.
  2. Simulate Communication as Social Interaction: Configure communication flows among nodes to denote social interactions like messaging, file sharing.
  3. Record Interaction Data: Track and log communication events among nodes like who communicates with whom, frequency of interaction.
  4. Calculate SNA Metrics: After executing the simulation, evaluate the trace file to calculate social network parameters such as centrality, clustering, and connectivity.
  5. Visualization and Analysis: Use tools to envision the network and measure interaction patterns according to the collected data.

Example: Simulating Social Network Interactions in NS2

In this sample, we will mimic a basic social network in which nodes denotes users, and their interaction patterns are used to evaluate social relationships.

Step 1: Define Nodes and Simulate Communication (Social Interactions)

# Define the simulator object

set ns [new Simulator]

# Define trace and nam files for output

set tracefile [open out.tr w]

set namfile [open out.nam w]

$ns trace-all $tracefile

$ns namtrace-all $namfile

# Define a ‘finish’ procedure to end the simulation and visualize in NAM

proc finish {} {

global ns tracefile namfile

$ns flush-trace

close $tracefile

close $namfile

exec nam out.nam &

exit 0

}

# Set up the topography for the simulation area

set topo [new Topography]

$topo load_flatgrid 500 500

# Define the wireless channel

set chan [new Channel/WirelessChannel]

# Configure nodes (representing social entities/users) using AODV routing protocol

$ns node-config -adhocRouting AODV \

-llType LL \

-macType Mac/802_11 \

-ifqType Queue/DropTail/PriQueue \

-ifqLen 50 \

-antType Antenna/OmniAntenna \

-propType Propagation/TwoRayGround \

-phyType Phy/WirelessPhy \

-channel $chan

# Create nodes representing social users

set node1 [$ns node]    ;# User 1

set node2 [$ns node]    ;# User 2

set node3 [$ns node]    ;# User 3

set node4 [$ns node]    ;# User 4

# Set node positions for NAM visualization

$node1 set X_ 100; $node1 set Y_ 100; $node1 set Z_ 0

$node2 set X_ 200; $node2 set Y_ 200; $node2 set Z_ 0

$node3 set X_ 300; $node3 set Y_ 100; $node3 set Z_ 0

$node4 set X_ 400; $node4 set Y_ 200; $node4 set Z_ 0

# Step 2: Simulate Social Interactions (Communication)

# User 1 communicates with User 2

set tcp1 [new Agent/TCP]

set sink1 [new Agent/TCPSink]

$ns attach-agent $node1 $tcp1

$ns attach-agent $node2 $sink1

$ns connect $tcp1 $sink1

# User 3 communicates with User 4

set tcp2 [new Agent/TCP]

set sink2 [new Agent/TCPSink]

$ns attach-agent $node3 $tcp2

$ns attach-agent $node4 $sink2

$ns connect $tcp2 $sink2

# Start communication (representing social interaction)

set ftp1 [new Application/FTP]

$ftp1 attach-agent $tcp1

$ftp1 start 1.0  ;# Start communication between User 1 and User 2 at 1 second

$ftp1 stop 5.0   ;# Stop communication at 5 seconds

set ftp2 [new Application/FTP]

$ftp2 attach-agent $tcp2

$ftp2 start 2.0; # Start communication between User 3 and User 4 at 2 seconds

$ftp2 stop 6.0   ;# Stop communication at 6 seconds

Step 2: Track and Record Communication Events

NS2 will systematically log all communication among nodes in the trace file (out.tr). This trace file will include elaborated packet transmissions details that can be evaluated to regulate who communicates with whom and the frequency of communication.

Step 3: Analyse the Trace File for Social Network Metrics

After the simulation is dome, evaluate the trace file to extract data regarding communication patterns like who interacts with whom, frequency of interaction, and time of interaction. We can use external tools such as Python or a simple script to estimate SNA parameters like:

  1. Degree Centrality: The number of connections (interactions) each node has.
  2. Closeness Centrality: How quickly a node can communicate with other nodes in the network.
  3. Betweenness Centrality: How usually a node serves as a bridge among other nodes.
  4. Clustering Coefficient: expand to which nodes tend to cluster together in groups.

We can parse the NS2 trace file (out.tr) to extract these parameters.

Step 4: Visualize the Network in NAM

To envision the network and communication in NAM, we can use the following command:

nam out.nam

In NAM, we will see the interaction flows among the nodes that denote social interactions.

Step 5: Post-Simulation Analysis Using Python or Other Tools

Once the simulation is done and we have the trace file, we need to evaluate the social communication using tools such as Python’s NetworkX library that is intended for graph-based social network analysis. We can extract the communication events from the NS2 trace file and convert them into a graph for additional analysis.

Example: Post-Simulation Analysis in Python

import networkx as nx

# Initialize an empty graph

G = nx.Graph()

# Read NS2 trace file and extract communication events

with open(‘out.tr’, ‘r’) as trace_file:

for line in trace_file:

if “tcp” in line:

parts = line.split()

src_node = parts[2]  # Assuming this is the source node ID

dst_node = parts[3]  # Assuming this is the destination node ID

G.add_edge(src_node, dst_node)  # Add interaction as an edge

# Calculate SNA metrics

degree_centrality = nx.degree_centrality(G)

closeness_centrality = nx.closeness_centrality(G)

betweenness_centrality = nx.betweenness_centrality(G)

# Print or visualize results

print(“Degree Centrality:”, degree_centrality)

print(“Closeness Centrality:”, closeness_centrality)

print(“Betweenness Centrality:”, betweenness_centrality)

# Visualize the network

nx.draw(G, with_labels=True)

Advanced Features for Social Network Analysis

  1. Dynamic Interaction: Establish node mobility or dynamic communication patterns in which the nodes move and communicate with diverse nodes over time.

Example:

$ns at 3.0 “$node1 setdest 250 250 5.0”

  1. Weighted Interactions: Allocate weights to interactions based on frequency or duration of communication that permits for more detailed SNA metrics.
  2. Group Dynamics and Community Detection: Use advanced SNA approaches to identify communities or subgroups in the network according to the communication patterns.
  3. Simulating Influence: To design effect propagation in the network in which particular nodes (influencers) can impact the behaviour of other nodes.

In the above procedure is complete procedure to implement the social network analysis in ns2 and also we deliver the key concepts, implementation procedures, sample codes and advanced features to implement the social network analysis. We plan to provide more information regarding the social network analysis. If you encounter any uncertainties regarding Social Network Analysis in the NS2 implementation, please don’t hesitate to reach out. We are here to assist you.