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Social Network Analysis for Business Applications

GK# 2696


Course Overview


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.


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Classroom Live Outline

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


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Classroom Live Labs

Exercises or hands-on workshops are included with most SAS courses.

Who Should Attend

  • 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
Course Delivery

This course is available in the following formats:

Classroom Live

Receive face-to-face instruction at one of our training center locations.

Duration: 2 day

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