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: For an overview of graph algorithms and their uses, see: https://neo4j.com/lp/book-graph-algorithms-ms/

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The Algorithm panel lets you run commonly used algorithms against your graph. The Path Finding tab lets you select Start and End nodes and click Trace Path to highlight the shortest path between the two sets of nodes.

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For a quick exploration of paths from a selected node, you can use Trace Neighbor. Its slider control shows the number of path steps in the graph, and lets you choose the number to highlight.

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Centrality algorithms include Degree, PageRank, Betweeness, Closeness, and Eigenvector. 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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Community Detection includes Louvain, Connected Component, Strong Connected Component, and Label Propagation algorithms. Again, click to run, and the property and its value for that algorithm is added to all the nodes in the graph.

Next Steps…