If the history of relational databases is any indication, what is going on in graph databases right now may be history in the making.

Three-point shooting, Steph Curry, and coming up with stories. If you feel like doing your own analysis to investigate hypotheses or discover insights at any level, RDF graph's got your back. Case in point: The NBA.

Graph databases are crossing the chasm to mainstream use cases, adding features such as machine learning to their arsenal and becoming more cloud and developer friendly. Last year was a breakout year, and graph database growth and evolution is well under way in 2019.

Querying blockchains is hard, but it does not have to be. This is the vision driving The Graph.

A new open-source library by Nvidia could be the secret ingredient to advancing analytics and making graph databases faster. The key: parallel processing on Nvidia GPUs.

Here's why and how a startup founded by a duo of researchers some months back is attracting big enterprise clients and serious funding

Google uses machine learning and graphs to deliver search results. Most search engines do not. Weaviate wants to change that.

A $325 million Series F funding round, bringing Neo4j's valuation to over $2 billion. A social network of 3 billion people, distributed across 1000 servers. The latter is a demo; the former is not. But both are real signs that the graph market and Neo4j are getting huge.

GraphQL is a specification that came at just the right time to address an age-old issue in software engineering: service integration. Apollo's implementation is seeing lots of traction, and it just got more gas in the tank for its grand vision that goes well beyond integration

Knowledge graphs are probably the best technology we have for data integration. But what about application integration? Knowledge graphs can help there, too, argues EnterpriseWeb.

GDS 2.0 and AuraDS from Neo4j bring graph data science one step closer to mainstream adoption.

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