mentatseb

It depends on your needs, maybe try the SaaS app https://kumu.io/ or https://graphcommons.com/

The tech story by NASA's chief knowledge architect is more detailed on https://linkurio.us/blog/how-nasa-experiments-with-knowledge... and https://neo4j.com/blog/nasa-critical-data-knowledge-graph/, with a presentation video on https://www.youtube.com/watch?v=vwJyU9vsfmU

Disclamer: Linkurious CEO here, the tool used to explore the Neo4j graph database used at NASA.

Yup, it's important that people understand the role of visualization in the complete data chain.

Hi, I'm Gephi+Linkurious co-founder. I've found visualizing large graphs pretty useless beyond the "I see meatballs!" effect and my opinion, after a decade in the field, is that it's the wrong problem for data analytics.

Much more interesting information is discovered during the process of dynamically building a visualization that is focused on user questions. I see with Linkurious that investigators usually need to visualize less than 1000 edges of a 1M+ edges graph to get answers.

It's basically applied actor-network theory from sociology and the delegation of control to objects https://en.wikipedia.org/wiki/Actor%E2%80%93network_theory

The conclusion is misleading due to 2 wrong assumptions:

1. The population is heterogeneous: interviews test different skills. All interviews don't test the same set of skills, which is mandatory to compare interview scores because scores are aggregates of these skill tests. Different job opportunities means different skills to test, so it seems reasonable to assume that people evaluation vary for different job opportunities, and thus their scores vary for different interviews.

2. The observations are not statistically independent: past interviews may influence future interviews. People may get better at passing interviews or conducting interviews over time. This would impact their score. It would be good to study the evolution of individual scores over time.

While (1) should strongly limit the conclusions of the study, the complete analysis may simply be irrelevant because of (2) if the statistical independence of observations is not demonstrated. Sorry guys but this is Statistics 101 introductory course.

Watch this conference talk about how to choose a graph visualization library: https://www.youtube.com/watch?v=7BPbaApIOrc

Disclamer: talk given by an engineer who has worked on the sigma.js fork https://github.com/Linkurious/linkurious.js

Thanks, I've fixed it.

Technical side: we recommend to display up to 2000 nodes and edges. Laptops < 2 year old can display and layout graphs up to 4000 nodes and edges but with stability issues.

Cognitive side: we recommend to hide nodes and edges as soon as you don't need them. One cannot ask the same class of questions to graph visualizations of very different sizes, see slide 19 on http://www.slideshare.net/Cloud/sp1-exploratory-network-anal...

The biggest dataset used by one of our users is a genetic graph of 240 millions of nodes and edges using a single server. Linkurious will take a few hours to index the complete dataset. From then, the search engine delivers instant results with autocomplete, fuzziness and advanced query options; graph exploration queries take less than a second to complete (sometimes a bit more depending on the web client). We are still working on improving our data indexing strategy to gain performance.

Synerscope has a strong approach to data analysis, and Danny Holten is well-known in the infovis community. I don't think that they provide a search engine thought, you have probably more information on their product.

TL;DR the README (https://github.com/Linkurious/linkurious.js), huh? The authors of the core/Sigma are cited in the documentation and license. Plus it is released under GNU GPLv3, so I don't understand why you complain. If you don't need the extra features, you are free to go with the core only.

After creating Gephi 8 years ago I'm still a big fan of open source as a production model, but definitely not as an economic model. Companies need support and high-end features to speed up their projects and to limit risks; this is what our tookit is about, adding more than 30 plugins.

Notice that we are happy to give back to the core when the original authors agree. Say thanks to us every time you click on an edge! :)

Cola has great algorithms! Hopefully they will be implemented as a Sigma plugin (https://github.com/qinfchen/sigmajs-webcola).

Still, GPU clusters beat all for performance. I'd love to see a demo :)

Thanks! Worth also mentioning Sigma.js, the open source core on which we are building our toolkit.

Thanks, our backend is based on node.js so we're considering to support the Rexster server (https://github.com/thinkaurelius/titan/wiki/Rexster-Graph-Se...), which provides a REST API to Gremlin. What do you think about it?

We're also eager to see any binary protocole to communicate with graph databases.

Licenses come with a 1-month money back guarantee. Drop me an email to [email protected] to discuss your project.

Ah thanks, I get it :)

Thanks for the suggestion! We have 2 different products, the second one being the developer toolkit (http://linkurio.us/toolkit/) so we ended up displaying the pricing on each different page... but we're still unsure about the clearest approach :/

Thanks for the feedback! I've created a ticket ;)

Hello folks,

We've just launched and we need your feedback. Thanks! :)

Seb

Btw, you can easily analyze your Facebook friend network:

http://blog.sociomantic.com/2010/05/using-netvizz-gephi-to-a...

Gephi is used to visualize graphs:

http://gephi.org/