GraphNews

A curated list of resources for graph-related topics, including graph databases, analytics and science - graphgeeks-lab/awesome-graph-universe

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-based data is ubiquitous in enterprise, across the industry verticals, and increasingly needed for machine learning use cases…

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.

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.

Graphs are everywhere. That has been the motto of graph aficionados for years, and now the world seems to be waking up to this.

vendors company,url,SPARQL,RDF-Star,Gremlin,Cypher,misc query,distrib,open source,parent,speaker,notes AgensGraph,a href="https://bitnine.net/agensgraph/"https://bitnine.net/agensgraph//a,Y,SQL,Bitnine AllegroGraph,a href="https://allegrograph.com/products/allegrograph/"https://allegrograp...

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

Nature Machine Intelligence - The number of graph neural network papers in this journal has grown as the field matures. We take a closer look at some of the scientific applications.

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?

Papers With Code highlights trending Machine Learning research and the code to implement it.

Memgraph, the streaming graph application platform, today announced Memgraph 2.0, the public launch of its source-available platform, which makes it easy

How simple structured data trumps clever machine learning

On the role of knowledge that is structured, explicit and intelligible in providing a path toward higher machine intelligence

"The UI allows individuals with no previous knowledge of the Semantic Web to query the DBpedia knowledge base...." > Interface to Query and Visualise Definitions from a Knowledge Base @anelia12430996 & Hélène De Ribaupierre https://t.co/QGSJSEq4Ab pic.twitter.com/EIhZRVikK0— Aaron Bradley (@aaranged) March 15, 2021

NetVec: A Scalable Hypergraph Embedding System https://t.co/CHaT76MA7T pic.twitter.com/ZVChTtb0zH— Aaron Bradley (@aaranged) March 18, 2021

Accurate cardinality estimates are a key ingredient to achieve optimal query plans. For RDF engines, specifically under common knowledge graph processing workloads, the lack of schema, correlated...

Basic Introduction to what Graph Analytics is…

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

Technology research and analyst house Bloor Research places Ontotext as champion among RDF graph providers in its latest update on the graph database market

Graph Databases are a great solution for many modern use cases: Fraud Detection, Knowledge Graphs, Asset Management, Recommendation Engines, IoT, Permission Management … you name it.  All such projects benefit from a database technology capable of analyzing highly connected data points and their relations fast – Graph databases are designed for these tasks. But the nature of graph data poses challenges when it comes to *buzzword alert* scalability. So why is this, and are graph databases capable of scaling? Let’s see... In the following, we will define what we mean by scaling, take a closer look at two challenges potentially hindering scaling with graph databases, and discuss solutions currently available. What Is the “Scalability of Graph Databases”? Let’s quickly define what we mean here by scaling, as it is not “just” putting more data on one machine or throwing it on various ones. What you want when working with large or growing datasets is also an acceptabl

https://douroucouli.wordpress.com/2019/05/10/obographviz-0-2-2-released-now-with-visualization-of-equivalence-cliques/

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