Network Analysis and Network Optimization in SAS(R) Viya(R)

Course code: VYNA01

This course provides a set of network analysis (graph theory) and network optimization solutions using the NETWORK and OPTNETWORK procedures in SAS Viya. Real-world applications are emphasized for each algorithm introduced in this course, including using network analysis as a stand-alone unsupervised learning technique, as well as incorporating network analysis and optimization to augment supervised learning techniques to improve machine learning model performance through input/feature creation.

The self-study e-learning includes:

  • Annotatable course notes in PDF format.
  • Virtual lab time to practice.

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Course dates

Starting date: Upon request

Type: E-learning

Course duration: 7 hours

Language: en

Price without VAT: 720 EUR


Starting date: Upon request

Type: Upon request

Course duration: 14 hours

Language: en

Price without VAT: 1 200 EUR


Type Course
Language Price without VAT
Upon request E-learning 7 hours en 720 EUR Register
Upon request Upon request 14 hours en 1 200 EUR Register
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Target group

Anyone interested in learning to incorporate network analysis and network optimization to provide solutions and solve real-world business challenges, including data scientists, business analysts, statisticians, and other quantitative professionals. Managers, directors, and leaders with a quantitative background are also encouraged to attend to learn how network analysis and optimization can be integrated into a broader portfolio of data science and machine learning applications.

Course structure

Concepts in Network Analysis

  • Introduction.
  • Network-level concepts.
  • Adjacency matrices and degree centrality.
  • Introduction to the NETWORK procedure.

Centrality Measures

  • Introduction.
  • Eigenvector centrality.
  • Betweenness and closeness centrality.
  • Influence centrality (self-study).
  • Hub and authority centrality.
  • PageRank centrality.

Analysis of Subnetworks

  • Connected and biconnected components.
  • Maximal cliques.
  • Community detection.
  • Paths, shortest paths, and cycles.
  • Pattern matching.

Bipartite Networks

  • Introduction to bipartite networks.
  • Network projection.

Network Optimization

  • Introduction.
  • Linear assignment problem.
  • Minimum spanning tree.
  • Maximum spanning tree (self-study).
  • Traveling salesman problem.
  • Minimum cost network flow (self-study).

Appendix A: Network Optimization Using the OPTMODEL Procedure

  • Total unduplicated reach and frequency (TURF) analysis.
  • Multiple traveling salesman problem (mTSP).
  • Minimum cost network flow.

Appendix B: Centrality Measures Using the IML Action Set

  • Introduction.
  • Eigenvector centrality using IML.
  • Hub and authority centrality using IML.
  • PageRank centrality using IML.


In order to complete practices with classroom software, attendees should have basic familiarity with statistics and mathematical concepts and be comfortable programming in SAS using DATA steps. Experience using macros is helpful, but not required.

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