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 SQL-GR™ (SQL-GR) framework. 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.
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.
- Vertices table
- Edges table
The following two tables represent the graph in the preceding figure.
In the following table, each row represents a vertex.
| 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.
| Source | Destination |
|---|---|
| A | B |
| A | C |
| A | E |
| B | D |
| C | D |
| C | F |
| C | G |
| E | C |
Iterations
When running graph functions, the SQL-GR iteration number in the log might differ from the function 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.