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Last year, we have announced our collaboration with Google Maps on deploying a graph neural network for estimating expected travel times (ETAs), which ... 31 comments on LinkedIn

Enterprises often find difficulty in applying insights from AI because of a lack of context, this is where knowledge graphs come in.

Ensuring the success of big graph processing for the next decade and beyond.

Within financial services, a lot of firms are still stuck in the past, using decades old tech to compete on a whole new data-driven playing field. These firms face growing challenges from regulators, and as part-fossilized data dinosaurs, they are threatened with extinction. In this paper, Capco’s Mark Kitson explores how knowledge graphs could be the answer to this problem.

How combining three emerging technologies is driving innovation

Ecstatic for our #IJCAI2021 research paper (14% acceptance)! Presenting TEC, a dynamic time-evolving graph neural network for capturing speaker language...

“Tutorial 7: Graph Neural Networks”...

Data may be the world’s most valuable resource, especially in financial services, which depends on harnessing insightful data. Structures are needed to make growing banks of data decipherable and useful, such as knowledge graphs—interlinked descriptions of relevant objects and their relationships and powerful aids in understanding data.

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My first ever blog post that doesn't include a single line of code. Hope you will enjoy my Complete guide to #node2vec algorithm blog post. #Graph #MachineLearning...

Introducing our next guest speaker at Knowledge Graphs in Drug Discovery Pt.2: Alex Ridden, CEO at Knights Analytics. Alex has almost 10 years experience...

Deep Learning on Graphs for Natural Language Processing (DLG4NLP) is a very fast-growing area in recent years. Our Graph4NLP library is designed as a powerful...

Happy to finally announce what I've been working on: an online course for GNNs! The Foundations of GNNs course will be more hands-on and community driven...

Mila is a place of collaboration and a meeting point for the main actors of artificial intelligence in Montreal. Our mission is to be a global pole for scientific advances that inspires innovation and the development of AI for the benefit of all.

Visualization recommendation or automatic visualization generation can significantly lower the barriers for general users to rapidly create effective data visualizations, especially for those...

Graph transformer networks (GTN) are a variant of graph convolutional networks (GCN) that are targeted to heterogeneous graphs in which nodes and edges have associated type information that can be...

With a Free 50GB Download...

These days, building and activating the network is a starting point to ensure adherence to standards and principles, quality of service and continuous ...

Noetic Cyber emerges from stealth with security monitoring using a graph database - SiliconANGLE

Graph Neural networks tutorial in Google Colab https://bit.ly/3yI9xBg #AI #DeepLearning #MachineLearning #DataScience...

A guide on how to create knowledge graphs using NER and Relation Extraction.

Recent years have seen a surge in research on deep interpretable neural networks with decision trees as one of the most commonly incorporated tools. There are at least three advantages of using...

We are proud to announce the release of ArangoDB 3.8! With this release ArangoDB improves many use-cases analytics.

This blog offers advice on selecting a direction of the graph relationships, supporting inverse relationships and naming a relationship.

Amazon Neptune is a fast, reliable, fully managed graph database service that makes it easy to build and run applications that work with highly connected datasets. Neptune’s database engine is optimized for storing billions of relationships and querying with millisecond latency. The W3C’s Resource Description Framework (RDF) model and the popular Labeled Property Graph model […]

Geometric Deep Learning is an attempt for geometric unification of a broad class of machine learning problems from the perspectives of symmetry and invariance. These principles not only underlie the breakthrough performance of convolutional neural networks and the recent success of graph neural networks but also provide a principled way…