What do you get when you combine two of the most up-and-coming paradigms in data processing -- streaming and graphs? Likely a potential game-changer, which DARPA and others are pivoting to invest in.

What does ESG mean in the balance sheet and beyond? How can advanced analytics help organizations evaluate their impact on sustainability?

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.

The advent of social networks, big data and e-commerce has re-emphasized the importance of analyzing a unique type of data structure- one…

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

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

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

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

We’re on a journey to advance and democratize artificial intelligence through open source and open science.

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.

Congratulations to @TigerGraphDB on being the first to successfully pass an LDBC SNB Business Intelligence workload audit on scale factor 1000. Their setup used the @AMD EPYC 9354 (Genoa) CPUs announced today. The results are available on the LDBC website.https://t.co/X3QocaTsRR pic.twitter.com/3F3J56wa96— Linked Data Benchmark Council (@LDBCouncil) November 10, 2022

Outlier Node Detection (OND) on graphs is widely used in financial fraudster identification, social network spammer detection, and so on. In NeurIPS 2022, we…

Identifying fraudulent behaviors is becoming increasingly more complex as technology advances and fraudsters constantly evolve new ways to exploit people, companies, and institutions. The complexity grows as companies introduce new channels, platforms, and devices for customers to engage with their brand, manage their accounts, and make transactions. Graph neural networks (GNN) are increasingly being used to identify suspicious behavior. GNNs can combine graph structures, such as email accounts, addresses, phone numbers, and purchasing behavior to find meaningful patterns and enhance fraud detection. In this video we will discuss: - Introduction to TigerGraph - Fraud Detection Challenges - Graph Model, Data Exploration, and Investigation - Visual Rules, Red Flags, and Feature Generation - TigerGraph Machine Learning Workbench: - XGBoost with Graph Features - Graph Neural Network and Explainability

#Google GUAC (Graph for Understanding Artifact Composition) Early stage, yet could change how the industry understands software #supplychains Free tool brings together sources of #software security metadata Collection - Ingestion - Collation - Query

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Ultipa: Reimagining Data Processing with Graph Augmented Intelligence Solutions/Service Company-Beijing, China; Singapore; California, U.S.A

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

Cambridge Core - Communications and Signal Processing - Introduction to Graph Signal Processing

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

[🥳new video🧠] You thought that the curvature of space and Ricci flow (famously used by Grisha Perelman to solve a 1.000.000$ millennium problem (Poincaré...

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

Knowledge graphs, the technology powering Google, Facebook and Apple, is now unlocking value across the financial sector. Knowledge graphs are transforming critical capabilities and enterprises are increasingly looking to the technology to enhance their tax strategy, perform compliance and improve customer service.

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.

In the last couple of months, I’ve been noticing a gradual shift in the kind of articles that we receive at Data Science Central. We still get a fair amount of data science content, but increasingly (and admittedly with a bit of encouragement) we’re seeing more articles centered around graphs and semantics. I don’t believe… Read More »DSC Weekly Digest 22 February 2022: Graphology

You’ve probably seen web editors based on the idea of blocks. I’m typing this in WordPress, which has a little + button that brings up a long list of potential blocks that you can inser…