Social Network Analysis Concepts Quiz

Social Network Analysis (SNA) is the study of social relationships and structures through networks and graph theory.

Nodes: Individual actors (people, organizations) within the network.
Edges: Connections or relationships between nodes (friendships, communications).
Graphs: Visual representations of networks where nodes are points and edges are lines connecting them.

Undirected Networks: Relationships without a defined direction (e.g., friendships).
Directed Networks: Relationships with a defined direction (e.g., Twitter followings).
Weighted Networks: Edges have weights representing the strength or value of the connection.

Degree Centrality: Number of direct connections a node has.
Betweenness Centrality: Frequency with which a node lies on the shortest path between other nodes.
Closeness Centrality: How close a node is to all other nodes in the network.
Eigenvector Centrality: Measure of a node's influence based on the importance of its connections.

Epidemiology: Understanding disease spread through social interactions.
Marketing: Identifying influencers within social networks.
Organizational Studies: Analyzing communication patterns and structures within companies.
Criminology: Investigating criminal networks and connections.

Graph Theory: Mathematical framework used to analyze networks.
Visualization Software: Tools like Gephi, NodeXL, or Pajek for visualizing networks.
Statistical Analysis: Using statistical methods to infer relationships and properties from network data.

Data Collection: Gathering accurate social network data can be difficult.
Dynamic Networks: Networks can change over time, complicating analysis.
Interpretation: Understanding the implications of network metrics requires context.

Social Network Analysis (SNA) utilizes networks and graph theory to examine social relationships and structures.

Nodes: Represent individual actors such as people or organizations in a network.
Edges: Define connections or relationships between nodes, exemplified by friendships or communications.
Graphs: Serve as visual tools to represent networks, where nodes are depicted as points and edges as connecting lines.

Undirected Networks: Feature relationships without a specific direction, typical in friendships.
Directed Networks: Include relationships with a specific direction, such as followers on social media platforms.
Weighted Networks: Assign weights to edges to indicate the strength or value of connections among nodes.

Degree Centrality: Counts the total number of direct connections linked to a specific node.
Betweenness Centrality: Measures how often a node appears on the shortest paths between other nodes, indicating its role as a connector.
Closeness Centrality: Evaluates the proximity of a node to all other nodes within the network.
Eigenvector Centrality: Assesses a node's influence based on the significance of its connections, not just the quantity.

Epidemiology: Analyzes how diseases spread through interpersonal social interactions.
Marketing: Identifies key influencers within social networks for targeted campaigns.
Organizational Studies: Studies communication patterns and structural dynamics within organizations.
Criminology: Investigates criminal networks to understand relationships and hierarchies among offenders.

Graph Theory: Provides a mathematical basis for analyzing and modeling network structures.
Visualization Software: Applications like Gephi, NodeXL, and Pajek facilitate network visualization and analysis.
Statistical Analysis: Employs statistical techniques to derive insights about relationships and attributes from network data.

Data Collection: Acquiring precise social network data poses significant difficulties.
Dynamic Networks: Changes in networks over time complicate the analytical process.
Interpretation: Contextual understanding is crucial for making sense of network metrics and their implications.

Increasing integration of machine learning and AI to process and analyze vast social network datasets.
Heightened attention on privacy and ethical issues surrounding data collection practices.
Investigation of online social networks and their influence on real-world behaviors and interactions.