Network Analysis and Network Optimization in SAS Viya

There is a new version of this course. Please see Network Analysis and Network Optimization in SAS Viya.

This course provides a set of algorithms to perform both network analysis and network optimization. Practical demonstrations and exercises emphasize the theory, and business case studies illustrate the possible outcomes from such techniques. Hands-on activities are based on the NETWORK and OPTNETWORK procedures in SAS Viya.

Network analysis includes graph theory algorithms that can augment data mining and machine learning. In many practical applications, pairwise interaction between the entities of interest in the model often plays an important role. Network analysis goes beyond traditional clustering and predictive models to identify patterns in business data, including entities’ behavior based on their relationships. Network analysis can be employed to avoid churn, diffuse products and services, detect fraud and abuse, identify anomalies, and many other applications, in a wide range of industries such as communications and media, banking, insurance, retail, utilities, and travel and transportation.

Network optimization includes graph theory algorithms that can augment more generic mathematical optimization approaches. Many practical applications of optimization depend on an underlying network. Networks also appear explicitly and implicitly in many other application contexts. Networks are often constructed from certain natural co-occurrence types of relationships, such as relationships among researchers who coauthor articles, actors who appear in the same movie, words or topics that occur in the same document, items that appear together in a shopping basket, terrorism suspects who travel together or are seen in the same location, and so on. In these types of relationships, the strength or frequency of interaction is modeled as weights on the links of the resulting network.

Apprendre à

• Identify the type of the data to create the nodes and roles in a network perspective, as well as the possible links between them.
• Define the weights for nodes and links and the methods to build a network on the available data, considering the distinct importance of nodes and links and their behavior in relation to each other.
• Recognize the different types of groups of nodes based on their relationships within the network, such as communities, connected components, biconnected components, core, and reach networks.
• Compute the network metrics such as degree, influence, closeness, betweenness, hub, authority, eigenvector, and clustering coefficient to analyze and describe the network.
• Perform network optimization based on a set of algorithms such as clique, cycle, shortest path, minimum-cost network flow, linear assignment, minimum spanning tree, minimum link-weight cut, transitive closure, and traveling salesman problem.
• Apply network analysis and network optimization to solve business problems in different industries.

A qui s’adresse cette formation ?

Data scientists, 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, and marketing modelers in all industries, including but not limited to communications and entertainment, banking and finance, insurance and retailers

Formats de formation disponibles	Durée standard (peut varier selon les sessions. Se référer au calendrier)
Formations présentielles:	1 jour

To complete exercises with classroom software, you should have at least a beginner-level background in statistics and mathematics and be minimally familiar with SAS programming.

Cette formation concerne SAS Viya logiciel

Concepts in Network Analysis

• Introduction.
• Concepts and properties.
• Small worlds and random graphs.
• Methods of analysis.

Analysis of Subnetworks

• Introduction.
• Connected components.
• Biconnected components.
• Community detection.
• Reach network.
• Core.

Network Metrics

• Introduction.
• Degree centrality.
• Influence centrality.
• Clustering coefficient.
• Closeness centrality.
• Betweenness centrality.
• Hub and authority.
• Eigenvector centrality.
• Metrics calculation by subgraphs.

Network Optimization

• Clique.
• Cycle.
• Linear assignment.
• Minimum-cost network flow.
• Minimum cut.
• Minimum spanning tree.
• Shortest path.
• Transitive closure.
• Traveling salesman problem.

Case Study: Communities' Layering

Case Study: Viral Effect in Portability