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SNA

From graph creation to influencer targeting, this repo offers practical tasks for optimizing strategies within budget constraints. Explore the synergy of networks and marketing outreach.

Social Network Analysis (SNA) for Marketing

Overview

This repository explores Social Network Analysis techniques for marketing strategies. With the provided 'connections.txt' data, the accompanying 'SNA.ipynb' notebook contains Python code snippets addressing various tasks, from graph creation to influencer targeting.

Tasks Covered

  • Graph Creation: Create a directed graph from the given data.
  • Identify Bridges: Detect nodes acting as bridges in the graph.
  • Graph Density: Calculate and interpret the graph density.
  • Node Connections: Highlight nodes with the highest and lowest connections.
  • In- and Out-Degrees: Display nodes with the highest incoming and outgoing connections.
  • Centrality Measures: Identify nodes with the highest closeness, betweenness, and eigenvector centrality, interpreting the findings.
  • Community Detection: Implement a community detection algorithm, showcasing the number of communities formed.
  • Largest and Smallest Communities: Identify and interpret the largest and smallest communities.
  • Community Visualization: Draw the three largest communities, remove top nodes based on centrality measures, and visualize each step.
  • Influencer Visualization: Draw influencers within the top communities.
  • Action Plan: Design a hypothetical action plan for a chosen business, considering budget and cost per action.

Files

connections.txt: Data file containing the list of edges.
SNA.ipynb: Jupyter notebook with Python code snippets for each task.
requirements.txt: Install necessary packages using pip install -r requirements.txt.