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3D Graph Network Topology Visualization

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Overview
Details
Graph Algorithms for Machine Learning Toolkit and custom visualisation for Splunk to plot relationships between objects with force directed graph based on ThreeJS/WebGL.

Find more information on the details page with link to blog articles like:
https://www.splunk.com/en_us/blog/machine-learning/chasing-a-hidden-gem-graph-analytics-with-splunk-s-machine-learning-toolkit.html

Information

You can find many more information around use cases on the following Splunk blogs:
- Chasing a Hidden Gem: Graph Analytics with Splunk’s Machine Learning Toolkit
- Understanding and baselining network behaviour - part 1
- Understanding and baselining network behaviour - part 2

Usage

| stats count by src dest [color_src] [color_dest] [edge_color] [weight_src] [weight_dest] [edge_weigth]

Replace src and dest with your fields to start. All other fields are optional and default values will be used if not given.

Optional values

Used to customise nodes and edges (or links) styling.

Field Name Type Description Example
color_src string Color of source node in HEX #00DD00
color_dest string Color of destination node in HEX #CC00FF
edge_color string Color of edge in HEX #12FF00
weight_src numeric Sphere size of source node 2.5
weight_dest numeric Sphere size of destination node 3
edge_weight numeric Stroke weight of edge line in pixels 2.1

Besides:

  • Field names must correspond to the ones specified above to be properly handled by the visualization
  • Any edge_weight value higher than 18 will be normalised to 18

Examples

Lookup tables

  • Add a lookup table defining these additional values to your Splunk instance. An example below.
$~ cat {your_lookup_table}.csv
source,color,weight
A,#010101,50
B,#ff0101,10
...
  • Execute your SPL
    {search} | stats count by src dest | lookup {your_lookup_table} source AS src | lookup {your_lookup_table} source AS dest OUTPUTNEW color AS color_dest, weight AS weight_dest

Simple SPL

  • Execute your SPL
    {search} | stats count as edge_weight by src dest | eval color_src="#cc0000", color_dest="#00ff00", weight_src=3 | eval edge_color=if(edge_weight < 18, "#0000cc","")

Graph Algorithm Examples

Four dashboards are provided to show graph algorithms in action using NetworkX. Select them by clicking on Graph Example Dashboards dropdown in the app navigation bar.

Requirements

Make sure you have the following Splunk apps installed in your instance to correctly visualize integrated dashboards.

Give global permissions to MLTK app if not already done

Contributing

This app is open sourced on github, so if you consider contributing to it, please refer to the contribution guidelines. Thanks in advance!

Thanks

Big thanks to Erica for making this custom visualisation real!
And a big thanks to Oliver for his awesome color gradient macros which are bundled in the app, too!
Finally many thanks to Greg for adding his work on the graph analytics workflow

Release Notes

Version 1.2.0
May 7, 2020

Added graph analysis workflow and dashboards
Bug fixes and improvements

Version 1.1.0
March 19, 2020

Added dashboard examples and NetworkX based graph algorithms for degree centrality, eigenvector centrality, betweenness centrality, clustering coefficients, connected components and label propagation. Splunk's Machine Learning Toolkit and the Python for Scientific Computing Package Version 2.0 required.

Version 1.0.3
Nov. 7, 2019

Version 1.0.2
July 22, 2019

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