top | item 15024859

PyGraphistry – A library to extract, transform, and visually explore big graphs

125 points| sndean | 8 years ago |github.com | reply

27 comments

order
[+] lmeyerov|8 years ago|reply
Fun to see a link here!

Always happy to describe what's happening underneath w/ connecting GPUs in the browser to GPUs in the datacenter. Likewise, the connection between event data & graph analytics is powerful as data scales, so happy to dig into that too.

Not shown there, we're piloting a 'visual playbook' investigation layer to help teams who investigate through a lot of event data. This has been especially relevant for security (SOC/IR/hunt) and anti-fraud as a team grows and needs to cover more ground. Playbooks let you finally record common multi-step multi-datasource workflows and get real visibility out of them. When your alerting flags something, running the playbook will gather & correlate that data for you, and unique to Graphistry, present it in a full visual (graph) analytics session. Think visually automating multi-step queries across Splunk + Spark + various APIs. And of course, for the advanced analysts, giant GPU-accelerated visualizations. We're actively piloting with interesting teams, so please ping [email protected] if it may be your team's kind of thing.

[+] ThePhysicist|8 years ago|reply
Is it possible to visualize graphs entirely on the client side (without sending any data to your backend)? We have some very large graphs that we'd like to explore, but unfortunately it's not possible to send the data to the cloud, hence a local solution would be great. I have investigated Gephi but unfortunately the performance is quite disappointing for very large graphs.
[+] lqdc13|8 years ago|reply
Is it possible to use a local GPU from the machine that has the data? Kind of like Gephi?
[+] technologia|8 years ago|reply
Personally from using it, it goes well beyond just visualization of large graphs. Its not readily useful to see so many nodes, but what they've done is made it easier for people to parse through large graphs without a lot of hand holding. I would point out the work of others in this field, like Marc Khoury (which @MurrayHill1980 has already kindly mentioned), but this points to making large graphs usable for a variety of folks in my opinion.
[+] stared|8 years ago|reply
Is it possible to run it locally? (i.e. without an API key)
[+] lmeyerov|8 years ago|reply
Yep! Feel free to give the cloud version a go, and if it looks useful, we can help get you going on-premise. That would include our investigation platform as well.
[+] lmeyerov|8 years ago|reply
I realized this is a great time to say -- we're hiring!

If you're into data visualization / UI engineering, fullstack node for data/security, or enterprise security sales, would love to chat. Our team is mostly in the bay area. If you've worked remotely before, that works great too.

We're especially growing in the security market around incident response + hunt. Our engineering work is around establishing more scalable best practices for investigation teams, building out our fullstack app, and we're in the middle of our next GPU visual analytics initiatives (accelerating interactive visual analytics another 100X!). So a lot of good stuff happening.

[+] m3nu|8 years ago|reply
Another plot.ly pushing their freemium product via Github? No pricing on the homepage yet. Will come later, after enough people integrated it in their projects.
[+] bravura|8 years ago|reply
Does anyone know a good tool to visually explore large ontology-type graphs?
[+] jvilledieu|8 years ago|reply
You may want to look into Linkurious: https://linkurio.us/ We provide a graph visualization interface that can connect to Neo4j, DataStax (DSE Graph), Titan or Allegrograph.

Disclaimer: I'm a co-founder of the company