# Overview of Graph Analysis Functions - Aster Analytics

## Teradata Aster® Analytics Foundation User GuideUpdate 2

Product
Aster Analytics
Release Number
7.00.02
Published
September 2017
Language
English (United States)
Last Update
2018-04-17
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uce1497542673292.ditamap
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B700-1022
lifecycle
previous
Product Category
Software

Typical uses of graph analysis functions are analysis of social and communications networks and fraud detection. Applications that use these functions include:

• Finding shortest paths
• Computing importance/influence scores
• Predicting unobserved variables based on knowledge of observed variables and network structures

The graph analysis functions use the Teradata Aster SQL-GR™ framework, which allows large-scale graph analysis in Aster Database. SQL-GR is based on a simple directed-graph data model, where each directed edge is represented as an ordered pair of vertices. To represent an undirected graph, use pairs of directed edges. For more information about SQL-GR, see Teradata Aster® Developer Guide.

## Graphs

A graph is a representation of interconnected objects. An object is represented as a vertex (also called a node)—for example, cities, computers, and people. A link connecting two vertices is called an edge. Edges can represent roads that connect cities, computer network cables, interpersonal connections (such as co-worker relationships), and so on.

Graph Example

Most Aster Analytics Graph functions represent a graph with two tables:
• Vertices table
• Edges table

The graph in the preceding figure is represented by the following two tables.

In the following table, each row represents a vertex.

Vertex Table Example
Vertex City Name
A Albany
B Berkeley
C Cerrito
D Danville
E East Palo Alto
F Foster City
G Gilroy

In the following table, each row represents an edge.

Edges Table Example
Source Destination
A B
A C
A E
B D
C D
C F
C G
E C

## Iterations

When running graph functions, the iteration number displayed in the Aster Management Console (AMC) is not same as the function’s iteration number. For example, when running the EigenvectorCentrality function, each EigenvectorCentrality iteration (EI) consumes 2 SQL-GR iterations (GI). For directed graphs, including the overhead, GI = 2*EI +1. For undirected graphs, GI = 2 * EI + 3.

For the PageRank function, the number of SQL-GR iterations equals the number of Pagerank iterations + 1.