GitHub

Simplifying Graph Convolutional Networks

This repo contains an example implementation of the Simple Graph Convolution (SGC) model, described in the ICML2019 paper Simplifying Graph Convolutional Networks. SGC removes the nonlinearities and ...
HackerNoon

The Race to Build AI’s Context Layer Is Really About Meaning

Context graphs, graph memory, and ontologies for AI are converging. What does this mean for enterprise AI in 2026?
IEEE

Self-Attention Graph Convolution Residual Network for Traffic Data Completion

Abstract: Complete and accurate traffic data is critical in urban traffic management, planning and operation. In fact, real-world traffic data contains missing values due to multiple factors, such as ...
IEEE

Efficient Residential Electric Load Forecasting via Transfer Learning and Graph Neural Networks

Abstract: The accurate short-term electric load forecasting (STLF) is critical for the safety and economical operation of modern electric power systems. Recently, the graph neural network (GNN) has ...
Frontiers

Optimizing large-scale graph database ingestion through edge value ranking: a proposed framework

1 Introduction Graph databases have become fundamental to modern enterprise applications, powering social network analysis, recommendation systems, fraud detection, and supply chain optimization. As ...
GitHub

matlab-deep-learning/SciML-and-Physics-Informed-Machine-Learning-Examples

Customer stories Events & webinars Ebooks & reports Business insights GitHub Skills ...
www.cs.cmu.edu

Tom Mitchell

For example, AI applications to medical diagnosis should be regulated very differently from AI applications to self-driving cars. U.S. National Academies report on AI and the Future of Work, study ...