GraphNews

Should you store business rules in your Enterprise Knowledge Graph (EKG)? This is a non-trivial question, and how you answer it might…

Excited to share our paper on graph representation learning in biomedicine and healthcare, published today in Nature Biomedical Engineering. In this…

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

GNNs derived as gradient flows minimising a learnable energy that describes attractive and repulsive forces between graph nodes.

Amazon Neptune is a fully managed graph database service that makes it easy to build and run applications that work with highly connected datasets. With Neptune, you can use open and popular graph query languages to execute powerful queries that are easy to write and perform well on connected data. You can use Neptune for […]

How graph technology can help strengthen your organization’s security program by providing the right level of context, visibility, and control over its APIs.

Structured Data chapter of the 2022 Web Almanac covering adoption and year on year change of RDFa, Opne Graph, Twitter, JSON-LD, Microdata, Facebook, Dublin Core, Microformats and microformats2 structured data.

Deep learning has become the dominant approach in coping with various tasks in Natural LanguageProcessing (NLP). Although text inputs are typically represented as a sequence of tokens, there isa...

#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

Limited Preview Query Translation Tool Is Now Available for Developers to Learn and Participate in the Development Process...

We introduce Saga, a next-generation knowledge construction and serving platform for powering knowledge-based applications at industrial scale. Saga follows a hybrid batch-incremental design to...

Summarizing in figures this excellent systematic survey of 507 papers on the state of #KnowledgeGraphs in #NLP since the first Internet-age KG was announced 10… | 12 comments on LinkedIn

We’ve all seen the signs in front of McDonald’s announcing “Over X Billion Served” and have watched the number rise over the years. But tracking how many burgers are sold every day, month, or year is a relic of the past. Today ask: Do we know where each consumer buys her burgers? At what time? What does she drink with it? What does she do before or after buying a burger? How can we satisfy more of her needs so that she keeps coming back? Datagraphs capture this information, helping to reshape competition in every sector. Leaders must invest in upgrading their data architecture to enable a real-time, comprehensive view of how consumers interact with their products and services so that they can develop unique ways to solve customer problems.

A powerful idea has been slowly building for many years now, originally known as the Semantic Web, and then later as Linked Data. This idea has finally... 27 comments on LinkedIn

iResearch

Learn about the Four Principles of Semantic Parsing: The Parser Principle, The Data Uncertainty Principle, The Data Entropy Principle, and The Principle of Deferred Semantics.

How Airbnb is leveraging graph neural networks to up-level our machine learning

Data Modeling and Analysis using Semantic Web Technologies

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In pace with developments in the research field of artificial intelligence, knowledge graphs (KGs) have attracted a surge of interest from both academia and industry. As a representation of...

Scientific Reports - Graph-based representation for identifying individual travel activities with spatiotemporal trajectories and POI data

Graph representations of programs are commonly a central element of machine learning for code research. We introduce an open source Python library python_graphs that applies static analysis to...

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

Fine-tuning innovations from many vendors

Why provenance is the key to AI success