Signal AI unveiled its new tool, a data structure that constantly tracks the major and minor events for companies that course through the news sphere each day.

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

Emerging graph database benchmarks are already helping to overcome performance, scalability and reliability issues.

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

TigerGraph's ML Workbench should enable data scientists to build deep-learning AI models using connected data directly from the business.

The Studio Search platform takes a portion of the federated graph and makes it searchable.

Knowledge graphs (KGs) are a novel paradigm for the representation, retrieval, and integration of data from highly heterogeneous sources. Within just a few years, KGs and their supporting technologie...

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This week, enterprise data catalog provider data.world announced both a $50 million Series C round and a new suite of data catalog management capabilities

With the right data fabric, people and algorithms can make better decisions and reduce the risk of data misuse or misinterpretation.

Some of the modern GNNs have their roots in methods originally developed in the signal processing domain.   Graph Signal Processing started with two directions...

Stardog Designer is our new, no-code, visual environment for data engineers and analysts to connect, map, model, and publish data. -- https://hubs.ly/...

𝖣𝖤𝖤𝖯 𝖫𝖤𝖠𝖱𝖭𝖨𝖭𝖦 𝖮𝖭 𝖦𝖱𝖠𝖯𝖧𝖲 332 Pages Free Pre-print download: https://lnkd.in/g7Eqg4Ag This book covers comprehensive contents in ...

With a $250,000 first prize, entrepreneurs, academics, engineers, scientists can create their own problem statement focused on a topic of their choosing.

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

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Graph databases will no longer be a luxury but will become a "must-have" for enterprise IT organizations in 2022, according to graph database provider TigerGraph. According to Gartner's research, by 2025, graph technologies will be used in 80% of new data and analytics systems, up from 10% in 2021, facilitating rapid decision-making across the enterprise.…

Pavel Klinov, Stardog VP of Research and Development, is back from the Dagstuhl Seminar on Graph Databases and Network Visualization, held at the Leibniz Center for Informatics in Germany from January 16 – 21, 2022. We asked him about his experience.

Can Machine Learning Do Symbolic Manipulation?  I spent some time over the holidays engaged in a fascinating online conversation. The gist of it was a variation of an argument that has been going on in the realm of artificial intelligence from the time of Minsky and Seymour Papert: Whether it is possible for neural networks to… Read More »DSC Weekly Digest 04 Jan 2022: Can Machine Learning Do Symbolic Manipulation?

How and why the best companies are adopting Graph Visual Analytics, Graph AI, and Graph Neural Networks. By Leo Meyerovich and Ben Lorica. [A version of this post originally appeared on the Graphistry blog.] In this post, we highlight the current state of Graph Intelligence, a new technology category around new tools and techniques forContinue reading "What is Graph Intelligence?"

Enterprise Knowledge Graph Platform ROI: Providing readers with a framework to evaluate the potential financial impact of deploying Stardog on their own organizations.

Using custom Named Entity Recognition and Relation Extraction models

The long-awaited major release of Bloom 2.0 has arrived with some key feature updates we think you’ll find helpful in your data exploration.

Bob Muglia, the former CEO of Snowflake, discusses what’s next for the tooling and technologies powering data analytics and engineering.

A blog about my work and fun with Neo4j, the world's leading graph database platform.

Excited that our graph Transformer has been accepted as a Contributed Talk to NeurIPS ML and Physical Sciences workshop (TOP 3 out of 151) :D Thanks to...

Last week Google added Graph Neural Network support to TensorFlow. This comes in the wake of PyTorch Geometric getting a lot of support from Meta (formerly...

Swedish unicorn Neo4j counts the likes of Adobe, AstraZeneca, Comcast, Ebay, and UBS among its growing clientele.

Papers With Code highlights trending Machine Learning research and the code to implement it.