Social Network Analysis for Business Applications



Go beyond the traditional clustering and predictive models to identify patterns in your business data. Social network analysis describes customers' behavior but not in terms of their individual attributes. Rather than basing models on static individual profiles, social network analysis depicts behavior in terms of how individuals relate to one another. In practical terms, this approach highlights connections between individuals and organizations and how important they might be in viral effect throughout communities and particular groups. For business purposes, social network analysis can be employed to avoid churn, diffuse products and services, and detect fraud and abuse, among many other applications.

In this course, you will learn how to build networks from raw data. You will also learn about the various approaches for analyzing your customers, focusing on their relationships and connections within the network. This course enables you to improve business performance and better understand how your customers are using products and services. In addition to the network analysis approach to linking distinct entities, playing different roles on particular connections, this course will also show you a set of network optimization algorithms that you can use to solve a variety of complex business problems. Methods such as minimum-cost network flow, shortest path, linear assignment, minimum spanning tree, eigenvector, and transitive closure are presented in a business perspective for problem solving.


  • Business analysts
  • Statisticians
  • Mathematicians
  • Network engineers
  • Computer scientists
  • Data analysts
  • Data scientists
  • Quantitative analysts
  • Data miners
  • Marketing analysts
  • Risk and fraud analysts
  • Analytical model developers
  • Marketing modelers in all industries, including but not limited to communications and entertainment, banking and finance, insurance, and retail


  • Beginners' level background in statistics and mathematics
  • Minimally familiar with SAS programming

Learning Objectives

  • Identify the type of data and the nodes and roles in a network perspective
  • Identify the type of data and the possible links between the actors within the network
  • Define the possible weight for nodes and links in a network perspective and the methods to build a network upon the data available, considering the distinguished importance of nodes and links within the network
  • Recognize the different types of groups and clusters of nodes based on their relationships within the network, such as communities, connected components, bi-connected components, core, cycle, and reach (ego) networks
  • Compute the network metrics such as degree, influence, closeness, betweenness, hub, authority, eigenvector, and clustering coefficient, and analyze the meaning of them while considering the business scenario, the industry involved, and the problem to solve
  • Perform network optimization based on several distinct algorithms like shortest path, minimum-cost network flow, linear assignment, eigenvector, minimum spanning tree, and transitive closure
  • Apply network analysis to solve real business problems in different industries

1. Fundamental Concepts in Network Analysis

  • Introduction
  • History of the network science
  • Concepts about network analysis
  • Random graphs and the small world
  • The type of data for network building and analysis
  • The structures of networks and how they evolve over time

2. Formal Methods for Network Analysis

  • How to identify and define nodes and links in different types of networks
  • Principal roles of the actors and their types of relationships
  • Statistical and mathematical approaches for network analysis
  • Graphical approach for network analysis
  • Modes and links correlation in the network analysis
  • Levels of measurement for network analysis
  • Modalities for network analysis
  • Scales of measurements in network analysis
  • Case study: Influence factor in telecommunications

3. Sub-Networks Detection and Analysis

  • Connected components
  • Bi-connected components
  • Community detection
  • Reach
  • Core
  • Cycle

4. Measures of Power in Network Analysis

  • Degree
  • Influence
  • Clustering coefficient
  • Closeness
  • Betweenness
  • Hub
  • Authority
  • Eigenvector

5. Graph Optimization

  • Minimum-cost network flow
  • Shortest path
  • Linear assignment
  • Minimum spanning tree
  • Eigenvector
  • Transitive closure

6. Business Applications Based on Network Analysis