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Leo Meyerovich
Leo Meyerovich
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The answer has changed a bit nowadays:

  • Analytics: For accelerated computing, cuGraphcuGraph (GPU-accelerated Python) as part of the broader RAPIDS Python GPU ecosystem. For many diverse algorithms, Neo4j has great extensions, and for potentially more scalable experiences, TigerGraph and GraphX. Graph neural nets are emerging as an exciting area but no clear best practice yet.

  • Visualization: GraphistryGraphistry (bias as the founder ;-)) is the only end-to-end GPU-accelerated visual graph analysis tool and runs in browsers even 5+ years old. Likewise, for use by analytics teams, it is also gaining popularity due to Python notebook support and built-in visual analytics (1K+ GitHub stars). D3/Linkurious/KeyLines/etc. are good for small graphs, with D3 being free. However, though for bigger ones, all of these are still much slower than Graphistry from 5 years ago, meaning you or your user's browser will crash on basic tasks like event data., and have little for already built-in visual analytics similar to desktop Gephi or beyond

The answer has changed a bit nowadays:

  • Analytics: For accelerated computing, cuGraph (GPU-accelerated Python) as part of the broader RAPIDS Python GPU ecosystem. For many diverse algorithms, Neo4j has great extensions, and for potentially more scalable experiences, TigerGraph and GraphX. Graph neural nets are emerging as an exciting area but no clear best practice yet.

  • Visualization: Graphistry (bias as the founder ;-)) is the only end-to-end GPU-accelerated visual graph analysis tool and runs in browsers even 5+ years old. D3/Linkurious/KeyLines/etc. are good for small graphs, with D3 being free, though for bigger ones, all of these are still much slower than Graphistry from 5 years ago, meaning you or your user's browser will crash on basic tasks like event data.

The answer has changed a bit nowadays:

  • Analytics: For accelerated computing, cuGraph (GPU-accelerated Python) as part of the broader RAPIDS Python GPU ecosystem. For many diverse algorithms, Neo4j has great extensions, and for potentially more scalable experiences, TigerGraph and GraphX. Graph neural nets are emerging as an exciting area but no clear best practice yet.

  • Visualization: Graphistry (bias as the founder ;-)) is the only end-to-end GPU-accelerated visual graph analysis tool and runs in browsers even 5+ years old. Likewise, for use by analytics teams, it is also gaining popularity due to Python notebook support and built-in visual analytics (1K+ GitHub stars). D3/Linkurious/KeyLines/etc. are good for small graphs, with D3 being free. However, for bigger ones, all of these are still much slower than Graphistry from 5 years ago, meaning you or your user's browser will crash on basic tasks like event data, and have little for already built-in visual analytics similar to desktop Gephi or beyond

Source Link
Leo Meyerovich
Leo Meyerovich
  • 1
  • 1

The answer has changed a bit nowadays:

  • Analytics: For accelerated computing, cuGraph (GPU-accelerated Python) as part of the broader RAPIDS Python GPU ecosystem. For many diverse algorithms, Neo4j has great extensions, and for potentially more scalable experiences, TigerGraph and GraphX. Graph neural nets are emerging as an exciting area but no clear best practice yet.

  • Visualization: Graphistry (bias as the founder ;-)) is the only end-to-end GPU-accelerated visual graph analysis tool and runs in browsers even 5+ years old. D3/Linkurious/KeyLines/etc. are good for small graphs, with D3 being free, though for bigger ones, all of these are still much slower than Graphistry from 5 years ago, meaning you or your user's browser will crash on basic tasks like event data.