| 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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| 1 | How To GraphXR 10. Algorithms |
| 2 | 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: * Download the file https://kineviz.com/s/GXR_QSG.zip (opens in a new tab), which contains the Game of Thrones data we're using in our tutorials. |
| 3 | 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. |
| 4 | The Path Finding tab provides an algorithm to highlight the shortest path between the two sets of nodes. |
| 5 | Select starting nodes and click Add to Start, then select ending nodes and click Add to End. Now click Trace Path. |
| 6 | 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. |
| 7 | Click Un-spotlight Path to see the entire graph again. TIP: You can Tag the spotlit path nodes or take a Snapshot. |
| 8 | 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). |
| 9 | 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. |
| 10 | 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... |
| 11 | 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. |
| 12 | You can open a Table to see the new properties, or an Enhanced Table to edit and export results. |
| 13 | 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. |
| 14 | Community Detection includes Connected Component, Strong Connected Component, Louvain and Label Propagation algorithms. |
| 15 | Again, click to run and a property and its value for that algorithm is added to all the nodes in the graph. |
| 16 | A thorough overview of graph algorithms and their uses is available in: https://neo4j.com/lp/book-graph-algorithms-ms/ (opens in a new tab) . Next, in Module 11. Editing, we'll dive into details of how to add and edit graph data. |