In this Session…

Before you begin…

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

  • Using Trace Neighbor.

To follow along, download:

HowTo_10START.graphxr

Slide

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How To GraphXR 10. Algorithms

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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:

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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. 

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

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

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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.

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

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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).

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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.

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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…

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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.

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

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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.

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

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

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A thorough overview of graph algorithms and their uses is available in: https://neo4j.com/lp/book-graph-algorithms-ms/ .

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

Next Steps…