GraphNews

I had a relatively hard time understanding Graph Neural Networks at first. Graph convolutional networks are based on graph spectral theory, which is not...

Machine Learning on Graphs: A Model and Comprehensive Taxonomy (https://lnkd.in/gASeEGRW) is an amazing paper that provides a taxonomy of the different...

Most demand forecasting algorithms are based on past consumption / offer pricing / safety stock etc. attributes. These algorithms do not fully model real-world...

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Understanding the building blocks and design choices of graph neural networks.

As part of the Concordia 2021 Annual Summit, U.S. Special Presidential Envoy for Climate John Kerry sits down with LinkedIn CEO Ryan Roslansky and Vice...

Very glad to announce that our paper on scalable graph neural networks for dynamic graphs has been accepted by the top conference in the supercomputing...

I’ve recently been playing around with a JDK data set, and with the release of Java 17 today, I wanted to explore this data set in a graph…

Data management strategy is boring, right? More to the point, it’s a solved problem. The relational model, SQL, and data warehouses date to the 1980s. I mean, what could be less strategic than the “what,” “how,” and “why” of an enterprise data management strategy? Build some pipelines and ETL jobs. Define a relational schema to…

This might seem like a trivial question at first, but it's not. Before we can evaluate whether a particular markup is actually helping Google understand the content of a web page, we need to make sure that Google can crawl and index its markup.

The new tool ecosystem for conceptual modeling with OntoUML. The full paper can be found in https://lnkd.in/evNpjBya #ontology #ontologies #conceptualmodeling...

This Tuesday in the LoGaG reading group, we will have Giorgos Bouritsas from Imperial College London and Andreas Loukas from École polytechnique fédérale...

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500 million+ members | Manage your professional identity. Build and engage with your professional network. Access knowledge, insights and opportunities.

This article discusses why benchmarking is important, as well as compare and contrasts Neo4j’s 100TB demo with TigerGraph.

Framework for zero-shot learning with knowledge graphs. - GitHub - BatsResearch/zsl-kg: Framework for zero-shot learning with knowledge graphs.

WikiGraphs: A Wikipedia Text - Knowledge Graph Paired Dataset Wang et al.: https://lnkd.in/dk8nCAjT #ArtificialIntelligence #DeepLearning #MachineLearning...

Criteo AI Lab is excited to be presenting Graph Machine Learning in Industry. Please join us on Thursday, September 23rd, at 17:00 Paris time. This page will be updated with video links after the workshop.

Keeping track of faculty research activities, using semantic data integration of public LOD sources combined into a single knowledge graph

The Data Exchange Podcast: Paco Nathan on recent trends in large-scale graph technologies and novel applications of graph databases.

What components are needed for building learning algorithms that leverage the structure and properties of graphs?

Stanford is proud to organize a workshop that  brings together leaders from academia and industry to showcase recent advances in Graph Neural Networks.... 45 comments on LinkedIn

Dr. Alicia Frame, director of graph data science at Neo4j, explains how graph technology could ameliorate the end of the third-party cookie

Yesterday I presented the paper "Graph? Yes! Which one? Help!" at the SEMANTiCS 2021 conference (co-authored with several colleagues from the Neptune team...

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I'm relatively old school, semantically speaking: my first encounters with RDF was in the early 2000s, not long after Tim Berners-Lee's now-famous article in Scientific American introducing the Semantic Web to the world.