6 years after, where are we?
Interested by your feedback concerning the evolution of the graph technologies landscape and about what the current landscape is.
https://lnkd.in/eEPkExH | 25 comments on LinkedIn
graphgeeks-lab/awesome-graph-universe: A curated list of resources for graph-related topics, including graph databases, analytics and science
A curated list of resources for graph-related topics, including graph databases, analytics and science - graphgeeks-lab/awesome-graph-universe
Awesome Graph Universe 🌐
Welcome to Awesome Graph Universe, a curated list of resources, tools, libraries, and applications for working with graphs and networks. This repository covers everything from Graph Databases and Knowledge Graphs to Graph Analytics, Graph Computing, and beyond.
Graphs and networks are essential in fields like data science, knowledge representation, machine learning, and computational biology. Our goal is to provide a comprehensive resource that helps researchers, developers, and enthusiasts explore and utilize graph-based technologies.
Feel free to contribute by submitting pull requests! 🚀
The Rise of Graph Jobs, The Disappearance of Graph Technology?
The Rise of Graph Jobs, The Disappearance of Graph Technology? Join me for this fun presentation at KGC on May 8, 2024, in NYC, where I delve into a "State of…
Graph, machine learning, hype, and beyond: ArangoDB open source multi-model database releases version 3.7
A sui generis, multi-model open source database, designed from the ground up to be distributed. ArangoDB keeps up with the times and uses graph, and machine learning, as the entry points for its offering.
From data to knowledge and AI via graphs: Technology to support a knowledge-based economy
In the new knowledge-based digital world, encoding and making use of business and operational knowledge is the key to making progress and staying competitive. Here's a shortlist of technologies and processes that can support this transition, and what they are about.
Amazon Neptune update: Machine learning, data science, and the future of graph databases
Amazon Neptune just added another query language, openCypher, to its arsenal. That may not sound like a big deal in and of itself, but coupled with updates in machine learning and data science features, it points towards the future of graph databases.
Graphs. Such a simple idea. Map a problem onto a graph then solve it by searching over the graph or by exploring the structure of the graph. What could be easier? Turns out, however, that working with graphs is a vast and complex field. Keeping up is challenging. To help keep up, you just need an editor who knows most people working with graphs, and have that editor gather nearly 70 researchers to summarize their work with graphs. The result is the book Massive Graph Analytics. — Timothy G. Mattson, Senior Principal Engineer, Intel Corp Expertise in massive-scale graph analytics is key for solving real-world grand challenges from healthcare to sustainability to detecting insider threats, cyber defense, and more. This book provides a comprehensive introduction to massive graph analytics, featuring contributions from thought leaders across academia, industry, and government. Massive Graph Analytics will be beneficial to students, researchers, and practitioners in academia, national
TigerGraph unveils new tool for machine learning modeling
TigerGraph unveiled a new tool that provides users with a dedicated, open source environment for building machine learning models with graph databases.
DSC Weekly Digest 04 Jan 2022: Can Machine Learning Do Symbolic Manipulation? - DataScienceCentral.com
Can Machine Learning Do Symbolic Manipulation? I spent some time over the holidays engaged in a fascinating online conversation. The gist of it was a variation of an argument that has been going on in the realm of artificial intelligence from the time of Minsky and Seymour Papert: Whether it is possible for neural networks to… Read More »DSC Weekly Digest 04 Jan 2022: Can Machine Learning Do Symbolic Manipulation?
London-based Memgraph raises over €8 million in seed funding to provide Streaming Graph Algorithms to the masses
Memgraph, the streaming graph application platform, today announced Memgraph 2.0, the public launch of its source-available platform, which makes it easy
We kicked off our #NeurIPS2020 series joined by @TacoCohen, ML Researcher at @Qualcomm @Qualcomm_Tech, to discuss his current research in equivariant networks and video compression using generative models, as well as his paper “Natural Graph Networks.”— The TWIML AI Podcast (@twimlai) December 22, 2020
"Inrupt ... has launched its first enterprise-ready Solid servers for use by more than a dozen partners, including the NHS, the BBC and NatWest Bank." https://t.co/HcW0uWJjUZ— Aaron Bradley (@aaranged) November 10, 2020
“How do you select a graph database? Learn how in @itworldca. Read here: https://t.co/Z529PWdeW6 | #GraphDatabase #GraphDB #GraphAnalytics #DataScience #Developer #Analytics #BigData”
TopQuadrant CEO, Irene Polikoff, provides an overview of the two main graph models along with illustrations of their similarities and differences in graph diagrams in Part I of II in this article series from @TDAN_com https://t.co/CxOrTb3ELL#knowledgegraphs #datagovernance— TopQuadrant (@TopQuadrant) September 25, 2020
I'll be discussing "Graph Queries with Gremlin Language Variants" at the Category Theory and Applications group meetup on October 6: https://t.co/MG1HpNEiGd Be prepared to see Gremlin in many different forms! #graphdb pic.twitter.com/OIOsfLvWze— stephen mallette (@spmallette) September 28, 2020
AI and Graph Technology: 4 Ways Graphs Add Context
Read the first installment of this blog series on artificial intelligence on the ways graph technology adds necessary context for powerful AI solutions.
