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

— Sergey Dorogovtsev (@dorogovtsev)

Excited to share that our recent work "Static Graph Approximations of Dynamic Contact Networks for Epidemic Forecasting" is published at Scientific Reports…

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…

See how TigerGraph has integrated their massively parallel processing graph database and analytics platform with NVIDIA cuGraph to deliver 100x performance

How to create synthetic datasets to improve graph-based anomaly detection models? Amazon researchers developed a new method for synthesizing training data for…

Links * Python Examples * JS Examples * YouTube Last week we highlighted LangGraph - a new package (available in both Python and JS) to better enable creation of LLM workflows containing cycles, which are a critical component of most agent runtimes. As a part of the launch, we highlighted two simple runtimes:

⚡ Building language agents as graphs ⚡

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

We have seen recently a surge in vector databases in this era of generative AI. The idea behind vector databases is to index the data with vectors that relate… | 30 comments on LinkedIn

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.

A new open-source library by Nvidia could be the secret ingredient to advancing analytics and making graph databases faster. The key: parallel processing on Nvidia GPUs.

Here's why and how a startup founded by a duo of researchers some months back is attracting big enterprise clients and serious funding

Google uses machine learning and graphs to deliver search results. Most search engines do not. Weaviate wants to change that.

GDS 2.0 and AuraDS from Neo4j bring graph data science one step closer to mainstream adoption.

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.

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.

Graph neural networks (GNNs) have emerged as one of the leading solutions for ML applications. Most real-world data can be represented as graphs - see this blog for a comprehensive overview of what use cases are best solved with GNNs and their key advantages.

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

TigerGraph unveiled a new tool that provides users with a dedicated, open source environment for building machine learning models with graph databases.

Information extraction, drug discovery, and software analysis are just a few applications of this versatile tool.

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?

Excited to share a Colab tutorial on graph neural networks with the JAX+Jraph stack, which we use in-house at DeepMind! Developed by Lisa Wang and Nikola... 12 comments on LinkedIn

How to tune the hyperparameters of FastRP to your specific problem

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