Research explainers on graph neural networks — GCNs, GATs, message passing, and graph-based representation learning.
GTP: The Graph-Transformer That Reads Whole Slide Pathology Images Like a Pathologist | AI Trend Blend AITrendBlend Machine Learning Computer Vision Medical AI About A team at Boston University built GTP — a Graph-Transformer for Pathology that fuses graph convolutional…
GTP: The Graph-Transformer That Reads Whole Slide Pathology Images Like a Pathologist Read More »
Causal Graph Neural Networks for Wildfire Forecasting Across Geographic Shifts | AI Trend Blend Shan Zhao, Ioannis Prapas, and their colleagues at TU Munich and the National Observatory of Athens built a causally informed graph neural network that forecasts burned…
Causal Graph Neural Networks for Wildfire Forecasting Across Geographic Shifts Read More »
In an era where financial transactions occur in milliseconds, social networks reshape human interaction by the minute, and traffic patterns shift with unpredictable urban dynamics, dynamic graph neural networks (DyGNNs) have emerged as essential tools for modeling real-world systems. Unlike…
In today’s rapidly evolving industrial landscape, product design and manufacturing systems (PDMS) face an unprecedented challenge: making sense of vast, interconnected data while dealing with incomplete knowledge bases. Knowledge graphs have emerged as the backbone of intelligent manufacturing, structuring complex…
A pathologist looking at a glioma slide is not reading one thing. She is reading how cells cluster into neighborhoods, how those neighborhoods sit inside the larger tissue, and how individual cells resemble each other under the microscope, all at…
AMGF-GNN: Adaptive Multi-Graph Fusion for Tumor Grading in Pathology Images Read More »
A pathology slide and a gene expression profile describe the same tumor from two completely different angles. One shows how the tissue is physically organized under a microscope, the other shows which genes are switched on or off inside it.…
Graph Attention Fusion of Pathology Images and Gene Expression Predicts Cancer Survival Read More »
In the age of information overload, the ability to process and interpret multi-modal data —such as text, images, videos, and audio—has become critical for artificial intelligence (AI) and machine learning (ML) systems. Traditional knowledge graphs (KGs), which represent information as…
Graphs are the backbone of modern machine learning systems—from recommender engines to protein interaction networks. But most Graph Neural Networks (GNNs) still rely on undirected topologies, ignoring the asymmetric and complex relationships prevalent in real-world data. This oversight results in:…
9 Explosive Strategies & Hidden Pitfalls in Data-Centric Directed Graph Learning Read More »
A radiologist looking at an MRI slice is not reading pixels one at a time. She is reading regions, how a bright patch near the pituitary gland relates to the tissue around it, whether an irregular margin near the frontal…
How Brain GCN Net Combines CNN and GNN for MRI Tumor Classification Read More »