16.5 Executing Built-in Algorithms

The graph server (PGX) contains a set of built-in algorithms that are available as Java APIs.

The following table provides an overview of the available algorithms, grouped by category. Note that these algorithms can be invoked through the Analyst Class.

Note:

See the supported Built-In Algorithms on GitHub for more details.

Table 16-11 Overview of Built-In Algorithms

Category	Algorithms
Classic graph algorithms	Prim's Algorithm
Community detection	Conductance Minimization (Soman and Narang Algorithm), Infomap, Label Propagation, Louvain
Connected components	Strongly Connected Components, Weakly Connected Components (WCC)
Link predition	WTF (Whom To Follow) Algorithm
Matrix factorization	Matrix Factorization
Other	Graph Traversal Algorithms
Path finding	All Vertices and Edges on Filtered Path, Bellman-Ford Algorithms, Bidirectional Dijkstra Algorithms, Compute Distance Index, Compute High-Degree Vertices, Dijkstra Algorithms, Enumerate Simple Paths, Fast Path Finding, Fattest Path, Filtered Fast Path Finding, Hop Distance Algorithms
Ranking and walking	Closeness Centrality Algorithms, Degree Centrality Algorithms, Eigenvector Centrality, Hyperlink-Induced Topic Search (HITS), PageRank Algorithms, Random Walk with Restart, Stochastic Approach for Link-Structure Analysis (SALSA) Algorithms, Vertex Betweenness Centrality Algorithms
Structure evaluation	Adamic-Adar index, Bipartite Check, Conductance, Cycle Detection Algorithms, Degree Distribution Algorithms, Eccentricity Algorithms, K-Core, Local Clustering Coefficient (LCC), Modularity, Partition Conductance, Reachability Algorithms, Topological Ordering Algorithms, Triangle Counting Algorithms

This following topics describe the use of the graph server (PGX) using Triangle Counting and PageRank analytics as examples.

• About Built-In Algorithms in the Graph Server (PGX)
  
• Running the Triangle Counting Algorithm
  
• Running the PageRank Algorithm
  

16.5.1 About Built-In Algorithms in the Graph Server (PGX)

The graph server (PGX) contains a set of built-in algorithms that are available as Java APIs. The details of the APIs are documented in the Javadoc that is included in the product documentation library. Specifically, see the BuiltinAlgorithms interface Method Summary for a list of the supported in-memory analyst methods.

For example, this is the PageRank procedure signature:

/**
   * Classic pagerank algorithm. Time complexity: O(E * K) with E = number of edges, K is a given constant (max
   * iterations)
   *
   * @param graph
   *          graph
   * @param e
   *          maximum error for terminating the iteration
   * @param d
   *          damping factor
   * @param max
   *          maximum number of iterations
   * @return Vertex Property holding the result as a double
   */
  public <ID extends Comparable<ID>> VertexProperty<ID, Double> pagerank(PgxGraph graph, double e, double d, int max);

16.5.2 Running the Triangle Counting Algorithm

For triangle counting, the sortByDegree boolean parameter of countTriangles() allows you to control whether the graph should first be sorted by degree (true) or not (false). If true, more memory will be used, but the algorithm will run faster; however, if your graph is very large, you might want to turn this optimization off to avoid running out of memory.

• JShell
• Java

opg4j> analyst.countTriangles(graph, true)
==> 1

import oracle.pgx.api.*;
 
Analyst analyst = session.createAnalyst();
long triangles = analyst.countTriangles(graph, true);

The algorithm finds one triangle in the sample graph.

Tip:

When using the graph shell, you can increase the amount of log output during execution by changing the logging level. See information about the :loglevel command with :h :loglevel.

16.5.3 Running the PageRank Algorithm

PageRank computes a rank value between 0 and 1 for each vertex (node) in the graph and stores the values in a double property. The algorithm therefore creates a vertex property of type double for the output.

In the graph server (PGX), there are two types of vertex and edge properties:

• Persistent Properties: Properties that are loaded with the graph from a data source are fixed, in-memory copies of the data on disk, and are therefore persistent. Persistent properties are read-only, immutable and shared between sessions.

• Transient Properties: Values can only be written to transient properties, which are private to a session. You can create transient properties by calling createVertexProperty and createEdgeProperty on PgxGraph objects, or by copying existing properties using clone() on Property objects.

  Transient properties hold the results of computation by algorithms. For example, the PageRank algorithm computes a rank value between 0 and 1 for each vertex in the graph and stores these values in a transient property named pg_rank. Transient properties are destroyed when the Analyst object is destroyed.

This example obtains the top three vertices with the highest PageRank values. It uses a transient vertex property of type double to hold the computed PageRank values. The PageRank algorithm uses the following default values for the input parameters: error (tolerance = 0.001), damping factor = 0.85, and maximum number of iterations = 100.

• JShell
• Java

opg4j> rank = analyst.pagerank(graph, 0.001, 0.85, 100);
==> ...
opg4j> rank.getTopKValues(3)
==> 128=0.1402019732468347
==> 333=0.12002296283541904
==> 99=0.09708583862990475

import java.util.Map.Entry;
import oracle.pgx.api.*;
 
Analyst analyst = session.createAnalyst();
VertexProperty<Integer, Double> rank = analyst.pagerank(graph, 0.001, 0.85, 100);
for (Entry<Integer, Double> entry : rank.getTopKValues(3)) {
 System.out.println(entry.getKey() + "=" + entry.getValue());
}