In this Session…

Before you begin…

  • Using Algorithms to understand paths and clusters in the graph.

  • Using Trace Neighbor.

To follow along, download:




How To GraphXR 10. Algorithms


Before You Begin…

Ideally, you’ll have worked through Module 9. Time Series. If you’re starting here, and you want to follow along, you’ll need to:

  • Log in to GraphXR, create a Project, and open its graph space.

  • Download starting data (HowTo_10_START.graphxr) for this module and drag and drop it onto the graph space. 

  • Optionally:


Open the Algorithm panel to run a variety of commonly used graph algorithms against your data.  Algorithms are organized in Path Finding, Centrality, and Community Detection tabs. 


The Path Finding tab provides an algorithm to highlight the shortest path between the two sets of nodes.


Select starting nodes and click Add to Start, then select ending nodes and click Add to End. Now click Trace Path


With a complicated graph it can be hard to see the path. You can click the Spotlight Path toggle to see only the nodes and edges involved in the path.


Click Un-spotlight Path to see the entire graph again.
TIP: You can Tag the spotlit path nodes or take a Snapshot.   


A brief aside: the Trace Neighbor toolbar icon provides another way to explore the paths from selected nodes (but not to a specified set of ending nodes).


Select one or more nodes and click the Trace Neighbor toolbar icon. A slider control shows the number of path steps in the graph, and lets you choose the number to highlight.


You can click Select Visible to select the highlighted path, then click Inverse and Hide Selected to hide the data not on the highlighted path. Now back to algorithms…


Centrality algorithms include Degree, PageRank, Betweenness, and Closeness. Simply click to run, and a property and value for that algorithm will be added to all the nodes in the graph.


You can open a Table to see the new properties, or an Enhanced Table to edit and export results.


We’ve already used the Degree algorithm and its gxr_degree property to filter out nodes with no connections from the graph. You can also use the property to focus on the most heavily connected nodes in the graph.


Community Detection includes Connected Component, Strong Connected Component, Louvain and Label Propagation algorithms.


Again, click to run and a property and its value for that algorithm is added to all the nodes in the graph.


A thorough overview of graph algorithms and their uses is available in: .

Next, in Module 11. Editing, we’ll dive into details of how to add and edit graph data. 

Next Steps…