Deep learning reveals nanoparticle shape from routine tracking analysis without new hardware

Researchers at the University of Tokyo and the Innovation Center of NanoMedicine (iCONM) have developed an artificial ...
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?

Cochlea network model reveals how inner ear may sort sound from noise

Over 70 million people in the U.S. are impacted by hearing loss, and age-related hearing loss is the second most common ...
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 ...
AlphaGalileo

Crossing Virtual Boundaries: Rethinking Social Network Alignment via Graph Learning

Sub-headline: HUST researchers systematize SNA methods, building an evolutionary taxonomy based on graph representation ...
IEEE

RNGCNs: Robust Norm Graph Convolutional Networks in the Presence of Missing Data and Large Noises

Abstract: Graph Convolutional Networks (GCNs) have been widely studied for attribute graph data learning. In many applications, graph node attributes/features may contain various kinds of noises, such ...
IEEE

Inconsistency-Aware Graph Convolutional Networks for Multi-View Classification

Abstract: Graph convolutional networks (GCNs) have attracted significant attention in the field of multi-view learning, as they effectively extract intricate information from diverse features.