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

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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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

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

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

GraphDB 9.10 features smart updates via SPARQL templates and Kafka as well as graph-path search optimizations

We believe that the future for cooperation in the construction industry is more based on data and not so much on files.

Build and Analyze Graph Database with Neo4j

The first end-to-end clinical knowledge graph creation using Spark NLP and Neo4j.

Memgraph, the streaming graph application platform, today announced Memgraph 2.0, the public launch of its source-available platform, which makes it easy

Types x Individuals, Instantiation x Specialization. These are among the most fundamental distinctions in Conceptual Modeling. Still, they seem to elude... 23 comments on LinkedIn

🤖 A Python library for learning and evaluating knowledge graph embeddings - GitHub - pykeen/pykeen: 🤖 A Python library for learning and evaluating knowledge graph embeddings

How the Semantic Web vision reincarnated in thousands of Linked Open Data datasets and millions of Schema.org tagged webpages

Exploring decentralized finance on the blockchain with a graph database

Learn how to combine Selenium and SpaCy to create a Neo4j knowledge graph of the Harry Potter universe

In the previous SPHERE news article focusing on the application of ontologies for digital twins of buildings, the importance of interoperability between data environments was stressed to reach a rich digital representation. In our philosophy, applications in the SPHERE ecosystem should always be replaceable to avoid vendor-lock in and to stimulate a best-of-breed approach. Such ... Read more

Until very recently, the objects of common use such as wearable devices, cars, watches, refrigerators and health-monitoring devices, did not produce or handle d

This article is an introduction into a field of graph-based code analysis. We will discuss a base concept of graph-based code analysis and learn how to build a Codebase Knowledge Graph (or Code Knowledge Graph or simply CKG) for a .NET Core project using Strazh.

Fixing the talent pipeline so that finding rocket scientists doesn't have to be rocket science

Learn how to visualize your intellectual property protection holistically to discover the gaps and evaluate their effects on dependent products and revenues.

Our latest benchmark report, Trillion Edge Knowledge Graph, is the first demonstration of a massive knowledge graph that consists of materialized data and Virtual Graphs spanning hybrid multicloud data sources. We prove it is possible to have a 1 trillion-edge knowledge graph and deliver sub-second query times while achieving a 98% cost savings.

A collection of important graph embedding, classification and representation learning papers with implementations. - benedekrozemberczki/awesome-graph-classification