The Algorithm panel provides a selection of computational graph algorithms widely used in graph analytics, organized in Path Finding, Centrality and Community Detection tabs. The Path Finding algorithm traces the path between a selected start and end node and applies an optional weight. Centrality and Community Detection algorithms return a numerical value for each node that can be used to distinguish groups or components in the graph.

For an e-book introduction to graph algorithms and their uses, see
Graph Algorithms: Practical Examples in Apache Spark and Neo4j.

Using the Path Finding Algorithm

The Path Finding algorithm traces the path between selected start and end nodes and applies an optional weight. The nodes and edges on the path are selected.

To find a path between connected nodes:

  1. Open the Algorithm panel and Path Finding tab.

  2. Select one or more starting nodes in the graph, and click Add to Start.
    Your selected nodes are listed in Start Nodes.
    Note: An error message displays if you select too many start and end nodes such that there are more than ten pairs for tracing the shortest path.

  3. Now select ending nodes in the graph, and click Add to End.
    The selected nodes are listed in End Nodes.
    Note: You can click Remove Start Nodes or Remove End Nodes to remove your current selection and choose other nodes.

  4. In the Weight Property menu you can select a property of one of the relationships in the path to be used as a path weighting value, or leave the default Ignore Weight Property selected. If the relationships have no properties (which is often the case), Ignore Weight Property will be the only choice.

  5. Click Trace Path to display and select the nodes and edges for the shortest path(s) between your start and end nodes.

  6. Once the path has been traced, you can:
    • Navigate the graph to inspect the path.
    • Save the selected data for further analysis, for example:
    - Tag the selected path.

    - Use Inverse and Hide Selected to temporarily hide data not on the path.

    - Save and share a Data View, save a GXRF file, or save and export a Snapshot.

Using Centrality or Community Detection Algorithms

When you run a Centrality or Community Detection algorithm the computational result is added as a property to each node in the graph. These data can be displayed (for example, in a scatter plot), accessed for other processes within GraphXR, or exported for use in statistical or graph analytics applications. The following table lists the available Centrality and Community Detection algorithms and the property names GraphXR uses to store the results.

Algorithm Type

Algorithm

Property Name

Centrality

PageRank

pageRank

Betweenness

betweenness

Closeness

closeness

Eigenvector

eigenvector

Community Detection

Connected Component

componentId

Strong Connected Component

strongComponentId

Louvain

louvainComponentId

Label Propagation

abelPropagationId

To run a Centrality or Community Detection algorithm:

  1. In the Algorithm panel, click to open the Centrality or Community Detection tab.

  2. Click the button for the algorithm you want to run.
    A message displays when the calculation is finished. The property and calculated value for the algorithm is now added to each node in the graph.

  3. To see the results, open a table, or any node’s info panel.