Social Network Analysis (SNA) is a method of investigating social structures through the use of networks and graph theory. It focuses on relationships between individuals, groups, or organizations, and how these connections influence behavior and information flow. 📊

Key Concepts

  • Nodes: Represent individuals, entities, or data points in the network.
    nodes
  • Edges: Symbolize relationships or interactions between nodes.
    edges
  • Centrality: Measures the importance of nodes within the network.
    centrality
  • Community Detection: Identifies clusters or groups of closely connected nodes.
    community_detection

Applications

  • 📈 Marketing: Analyze customer interactions and spread of influence.
  • 🛡️ Cybersecurity: Detect anomalies or threats in network behavior.
  • 📚 Academic Research: Study collaboration patterns among scholars.
    academic_research

Tools & Techniques

  • Gephi for visualizing complex networks.
  • Python libraries like NetworkX and igraph.
  • Community detection algorithms (e.g., Louvain Method).

For deeper insights into SNA tools, visit our dedicated documentation.

social_network_analysis_tools