GraphNews

Stardog 5.3 includes 5x cluster performance improvement and S3 backups. Read on for the details.

A blog about my work and fun with Neo4j, the world's leading graph database platform.

Interactive visualization of the Gremlin graph database with D3.js - bricaud/graphexp

David and Jeff talk with Ted Wilmes from Expero about best practices regarding DSE Graph and the importance of proper data modeling. Highlights 0:16 - David goes through introductions and gets an answer about who/what is Expero 0:57 - Expero has the best swag and David realizes his laptop is already wearing some 2:13 - What does Expero help developers understand? 3:55 - Ted explains why core DSE data modeling is so important because everything you do in DSE Graph eventually maps down to your model 4:39 - Potential graph pitfalls from not understanding implications of the underlying data mod...

Just recently A* Algorithm was added to Neo4j graph algorithms and I decided to show how nicely APOC spatial functions fit with it as it uses GPS location for heuristic. Import I found this cool gi…

Recently similarity algorithms were introduced in Neo4j graph algorithms library, so I decided to show how easy it has become to infer a graph using Jaccard similarity and then run Community detect…

Semantic knowledge graphs accelerate data engineering for machine learning, helping you maximize results.

Cosmos DB is undoubtedly one of the most interesting products in Microsoft’s Azure portfolio. It’s a fully managed, globally distributed multi-model database that offers throughput guarantees, a number of different consistency models and high read and write availability guarantees. Now that’s a mouthful, but basically, it means that developers can build a truly global product, […]

At linked data-driven companies you will find it difficult to distinguish between “traditional” data workers and those in other functional areas who, at other companies, are less reliant on data.

Graph analysis is not a new branch of data science, yet is not the usual “go-to” method data scientists apply today. However there are some…

Same neo4j linear regression procedures, now unlimited independent variables! More functionality without additional complexity for the…

Interesting piece. Many seem to be feeling around blindly for the next unicorn, when new value from data is being created and discussed openly by companies who blend statistical and knowledge modeling. Some examples: https://t.co/V3QCVLSmBT https://t.co/aOvQnYz1XV— Alan Morrison (@AlanMorrison) November 17, 2018

Football clubs connected to the countries for which their players play at the world cup. Size of nodes: number of players at the world cup. pic.twitter.com/ONYjuCafUn— Alexandre Afonso (@alexandreafonso) June 17, 2018

The Graph Engine Service from Huawei Cloud is Apache TinkerPop enabled - yet another #graphdb to offer Gremlin support https://t.co/azSWcmDKUP pic.twitter.com/tnaUNORXSf— TinkerPop (@apachetinkerpop) July 18, 2018

“Lovveeeee these women. They inspire me!!! Thank you for coming and speaking at the Neo4j Ecosystem Summit!!!! @gabidavila (Developer Advocate, Google), @reshamas (Data Scientist and Organizer of WiMLDS, PyLadies NYC), @jumokedada (Founder, Tech Women Network) #Neo4j #GraphConnect”

New release of neosemantics for #neo4j is now available (3.4.0.2). It includes model mapping and microinferencing capabilities. Watch this space for examples of use... https://t.co/il4UuzC5zF #RDF— Jesús Barrasa (@BarrasaDV) October 10, 2018

“#RDF4J v2.3 released, JDK9 compatible, Skolemization, IRI validation, improved order/limit performance, better TTL pretty-print, many bugfixes https://t.co/FJpPJrdYrp https://t.co/Y4k7kiIkWz #semweb #java”

Recently I decided to teach myself #React and #electron so I built out a IDE for running traversals and visualizing results for @apachetinkerpop enabled databases. If you want to try it out you can get it here: https://t.co/Yb6eaF6HKo #graphDB #graph— Dave Bechberger (@bechbd) August 23, 2018

If you find https://t.co/XZ7CIl9C8r interesting, knowledge graphs the next generation of search, & linked data a place where the web becomes a very useful database, you might want to check out this new site from the creators of https://t.co/XZ7CIl9C8r: https://t.co/bhnxXR1grI— Bill Slawski ⚓ (@bill_slawski) October 18, 2018

Old-school symbolic AI is sneaking back into modern machine learning AI, and the heritage of triples-based inferencing shows through in some of it. Nice short presentation by Imperial College London's Marta Garnelo: https://t.co/4nelxJmsYu pic.twitter.com/fSftB5Tqr9— Bob DuCharme (@bobdc) June 24, 2018

New blog entry "Extracting RDF data models from Wikidata". https://t.co/B5iWE4zWN7— Bob DuCharme (@bobdc) November 18, 2018

RDF was designed as a data interchange framework; what you do in the privacy of your own database is your own business— Dan Brickley (@danbri) September 16, 2018

Semantic Webby projects could often do with a dose of this. I'm afraid that careful UI creation suffers from semweb culture valuing generality over all else.Perhaps SHACL & ShEx shapes will provide better attachment points for UIs built over graph data? https://t.co/C1xvAoxPiU— Dan Brickley (@danbri) May 19, 2018

Gartner serves up 2018 Hype Cycle with a heavy side of AIImage Source: https://t.co/90NoF4IqvG#AI #MachineLearning #DeepLearning #BigData #Fintech #Insurtech #Marketing #Datascience #ML #DL #Robotics #HealthTech #IoT #tech Article Link: https://t.co/CtQlfve1Gm pic.twitter.com/Y5Xelzc6r4— AI (@DeepLearn007) August 21, 2018

New @ManningBooks early access book: "Graph-Powered Machine Learning" by Dr. Alessandro Negro @AlessandroNegro #MachineLearning #neo4j #AmazonNeptune #DataScience https://t.co/XpxZK3jw0g pic.twitter.com/xntecIh0Yh— John Dhabolt (@Dhabolt) October 17, 2018

Here are our slides for #stratadata on the progress we've made on scalable vulnerability discovery with code property graphs: multi-layered, extensible and with support for multiple programming languages: https://t.co/qbgK5PM28m pic.twitter.com/iRjLfr8VY2— Fabian Yamaguchi (@fabsx00) May 23, 2018

I think graphs are the correct mental model and API for deep learning -- but not graph of ops like a TF graphdef, instead, graph of layers. Recursive graphs of high-level building blocks.Which is also how deep NNs are visualized in pretty much every paper or textbook ever...— François Chollet (@fchollet) November 15, 2018

Sorry, @neo4j, but I gotta ask. What's wrong with #sparql? pic.twitter.com/JkrF7uck6V— Florent Georges (@fgeorges) May 15, 2018