GraphNews

Like many of you, I’ve been reading a lot lately about ChatGPT and LLMs, and how they will impact various aspects of our society. Some of…

North Carolina-based Fluree, which offers a blockchain-backed graph database, announced $10 million in series A funding.

Learn how to develop a LangChain agent that has multiple ways of interacting with the Neo4j database

Recommendation systems have become an essential component of modern technology, used in various applications such as e-commerce, search engines, and chat assistants. In this blog post, we’ll …

Researchers develop a framework to capture the vast downstream impact and complex upstream dependencies that define the foundation model ecosystem.

I thought, briefly, of doing another big animated piece for this issue of The Cagle Report. Still, I suspect I’m...

𝐔𝐧𝐥𝐞𝐚𝐬𝐡𝐢𝐧𝐠 𝐭𝐡𝐞 𝐏𝐨𝐰𝐞𝐫 𝐨𝐟 𝐆𝐫𝐚𝐩𝐡 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬: 𝟐𝟓 𝐓𝐨𝐩 𝐏𝐲𝐭𝐡𝐨𝐧 𝐋𝐢𝐛𝐫𝐚𝐫𝐢𝐞𝐬, 𝐀𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦𝐬, 𝐓𝐲𝐩𝐞𝐬 𝐚𝐧𝐝… | 29 comments on LinkedIn

ChatGPT and Generative AI have become, seemingly overnight, two of the hottest topics being discussed in tech circles. Some of...

A rule-based data query language that thinks differently

Use GPT-4 as a domain expert to help you extract knowledge from a video transcript

It is widely recognised that Large Language Models, such as GPT, are built on the Transformer architecture. However, what may be less widely acknowledged is… | 83 comments on LinkedIn

Attached is my presentation from a graph analytics talk I gave in London for G-Research. It was a delight, and I met some brilliant people with tough questions… | 28 comments on LinkedIn

AI has revolutionized the way we model complex systems. From dynamic networks in biology to interacting particle systems in physics, AI for graphs has achieved… | 18 comments on LinkedIn

Any database is pretty useless unless the user can ask, and get answers to, their questions.

A new milestone in graph data management

When Apache Spark became a top-level project in 2014, and shortly thereafter burst onto the big data scene, it along with the public cloud disrupted the big data market. Databricks Inc. cleverly opti

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Attention LinkedIn network! Did you know that GPT is learning from web pages and that over 40% of those web pages contain islands of JSON-LD? These… | 50 comments on LinkedIn

In today's data-driven world, it is crucial to establish a clear boundary between your public and private data. Whilst banks, medical institutions, and spy… | 35 comments on LinkedIn

Co-authors: Sofus Macskássy, Carol Jin, Shiyong Lin, Xiaomin Wei, and Michael O’Neill

Two experts go head-to-head – then you decide

We present the Graph Forward-Forward (GFF) algorithm, an extension of the Forward-Forward procedure to graphs, able to handle features distributed over a graph's nodes. This allows training graph neural networks with forward passes only, without backpropagation. Our method is agnostic to the message-passing scheme, and provides a more biologically plausible learning scheme than backpropagation, while also carrying computational advantages. With GFF, graph neural networks are trained greedily layer by layer, using both positive and negative samples. We run experiments on 11 standard graph property prediction tasks, showing how GFF provides an effective alternative to backpropagation for training graph neural networks. This shows in particular that this procedure is remarkably efficient in spite of combining the per-layer training with the locality of the processing in a GNN.

TigerGraph, provider of an advanced analytics and ML platform for connected data, is releasing the latest version (3.9) of TigerGraph Cloud, the native parallel graph database-as-a-service. TigerGraph Cloud 3.9 includes new security, advanced AI, and machine learning capabilities that meet the demands of its rapidly growing customer base and streamline the adoption, deployment, and management of the most scalable graph database platform, according to the company. The underlying parallel native graph database engine is also available for on-prem or self-managed cloud installation.

AI-chatbot software for complex requirements

If you're thinking about building Data Products in your organisation then you need to know about JSON-LD! JSON-LD, short for JavaScript Object Notation for… | 27 comments on LinkedIn

Geometric Deep Learning is a term for approaches considering ML problems from the perspectives of symmetry and invariance. It provides a common blueprint for CNNs, GNNs, and Transformers. Here, we study the history of GDL from ancient Greek geometry to Graph Neural Networks.

As promised by many analysts, the automatic creation of knowledge graphs should have allowed us to reach the Holy Grail of knowledge…

It can be hard to work out from Wikipedia if an Ontologist is a philosopher, a physicist or something completely different