AlphaGalileo

AdapGNN: A New Framework Enhances Explainability of Graph Neural Networks for Molecular Property Prediction

Researchers have developed AdapGNN, a novel model-agnostic framework that addresses the oversmoothing problem in graph neural ...
theregister

Neo4j plots Palantir alternative with GraphAware acquisition

"The no-kill-switch kind of thing? It's increasingly becoming a requirement," says Neo4j CEO Emil Eifrem. This is one of the reasons behind the company's decision to buy GraphAware, an intelligence ...
manilatimes

Zscaler to Acquire Symmetry Systems, Combining Zero Trust and Access Graph Technology to Map and Secure AI Agent Communication

Symmetry Systems gives Zscaler the foundational visibility required to govern AI agent communication at scale and unlock the next phase of Zero Trust SAN JOSE, Calif., May 21, 2026 (GLOBE NEWSWIRE) -- ...
IEEE

GSPNet: Graph Spectral Projection Network using Learnable Spectral Transformation

Abstract: Spectral graph convolutional networks (SGCNs) are one of the leading tools to handle learning tasks with graph structure. SGCNs leverage graph structure to define the graph spectral ...
GitHub

Neo4j Graph Data Science

This repository hosts the open sources of the Neo4j Graph Data Science (GDS) library. The GDS library is a plugin for the Neo4j graph database. GDS comprises graph algorithms, graph transformations, ...
Hosted on MSN

Using transformations and the parent graph to graph an exponential function

👉 Learn how to graph exponential functions involving vertical shift. An exponential function is a function that increases rapidly as the value of x increases. To graph an exponential function, it is ...
Hosted on MSN

How to graph an absolute value equation using transformations

👉 Learn about graphing absolute value equations. An absolute value equation is an equation having the absolute value sign and the value of the equation is always positive. The graph of the parent ...
GitHub

KGTK: Knowledge Graph Toolkit

The Knowledge Graph Toolkit (KGTK) is a comprehensive framework for the creation and exploitation of large hyper-relational knowledge graphs (KGs), designed for ease of use, scalability, and speed.
Frontiers

MicrobiomeKG: bridging microbiome research and host health through knowledge graphs

The microbiome represents a complex community of trillions of microorganisms residing in various body parts and plays critical roles in maintaining host health and wellbeing. Understanding the ...
Diginomica

Blue Yonder aims for ‘one speed’ supply chains with new AI and knowledge graph updates

Blue Yonder’s latest product updates are a culmination of work that’s been happening behind the scenes. The foundations are now in place for buyers to reconsider how they think about their supply ...
IEEE

Temporal Knowledge Graph Embedding using Householder Transformations

Abstract: The rapid development of Knowledge Graph (KG) technology has led to the emergence of Temporal Knowledge Graphs (TKGs), which hold significant research importance and value. Temporal ...