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Interested in explainability for graph neural networks? Check out this list created by our team in AstraZeneca (Gavin E. and Sebastian Nilsson)! https:...

Check out all the latest stories, product features and special promo offers from Careem.

Deep Learning on Graphs for Natural Language Processing (DLG4NLP) is a very fast-growing area in recent years. Welcome to attend our DLG4NLP tutorial...

Devs: Semantic Web is so dead Amazon: provides a Linked Data API for Alexa with JSON-LD support https://t.co/ghLLiFOsWk

This article is an introduction into a field of graph-based code analysis. We will discuss a base concept of graph-based code analysis and learn how to build a Codebase Knowledge Graph (or Code Knowledge Graph or simply CKG) for a .NET Core project using Strazh.

The success of neural network embeddings has entailed a renewed interest in using knowledge graphs for a wide variety of machine learning and information retrieval tasks. In particular, recent...

Using knowledge-infused learning to integrate knowledge graphs and machine learning can lead to improvements in autonomous driving. Six grand opportunities.

Vesoft Inc. is planning a new funding round that could bolster the Chinese graph database technology startup’s valuation to almost $1 billion, according to its founder and Chief Executive Officer Sherman Ye.

Machine learning tools are used to greatly accelerate chip layout design, by posing chip floorplanning as a reinforcement learning problem and using neural networks to generate high-performance chip layouts.

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Fixing the talent pipeline so that finding rocket scientists doesn't have to be rocket science

Mapped, which simplifies access to building assets via a digital twin API, raised $6.5 million in a seed II round.

This is the first article in a four-part series on graph theory and graph neural networks. It explains graph theory in machine learning, and how it’s changed the game.

Our e-book aims to shed light on what we believe is a real game-changer for those looking to improve upon simplistic answers sometimes arrived at by using traditional ML algorithms and approaches. We show how you are able to combine the power of both graphs and ML (in a variety of different ways) to help you arrive at better answers compared to using standard ML approaches alone.

TigerGraph came out of stealth in 2017, and every year since has been coined “The Year of the Graph” by experts, journalists, and market watchers due to the accelerating momentum.  2018, 2019, and 2020 each had incremental “Year of the Graph” potential. In those years, more and more enterprises adopted graph at scale for increasingly […]

With data scientists in high demand, NASA is pursuing a pioneering approach to filling its skills gaps.

Graph attention, graph convolution, network propagation are all special cases of message passing in graph neural networks.

Putting value on relationships.

Here I am trying to build a script that process the short scientific texts (abstracts) and finds Wikidata items corresponding to the terms. An interactive and editable table is also created to allow an editor to validate the found matches and find other related items. A bit amateurish attempt by a Wikidata newbie.

Commentary: Relational databases aren't dead, but database innovation is cropping up in all sorts of places now, including graph databases.

Fridays give away! Graph Machine Learning: Take graph data to the next level by applying machine learning techniques and algorithms. Like and comment ... 75 comments on LinkedIn

"Very Deep Graph Neural Networks Via Noise Regularisation" by Petar Veličković, Yulia Rubanova, Alvaro Sanchez Gonzalez, Jonathan Godwin, et al. Paper...

Iconology is a branch of art history that investigates the meaning of artworks in relation to their social and cultural background. Nowadays, several interdisciplinary research fields leverage...

We describe a novel method for identifying and extracting conceptual components from domain ontologies, which are used to understand and compare them. The method is applied to two corpora of...

Explainability techniques for Graph Neural Networks still have a long way to go compared to explanations available for both neural and decision decision tree-based models trained on tabular data....

Knowledge graphs are getting more mature and very practical in many different domains. From enabling data mesh architecture to disaster resilience, knowledge...

Using R to extract user data from the Yelp API and create network graphs

Mapping each node to an embedding vector results in enormously large embedding matrices. Is there a way to have a fixed-size vocabulary of…