Research explainers on graph neural networks — GCNs, GATs, message passing, and graph-based representation learning.
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…
Cancer grading sounds like a simple counting exercise. Look at how abnormal the cells are, assign a number, done. In practice grading is closer to reading a crowded room and trying to describe its social structure at three different zoom…
AMGF-GNN: Adaptive Multi-Graph Fusion for Tumor Grading in Pathology Images Read More »
Knowledge graph completion sounds abstract until you picture the actual data sitting in one of these graphs. An entity might have a blurry thumbnail, a two sentence description that was scraped and never edited, and a handful of relations that…
AFME Teaches Knowledge Graphs Which Modality To Trust Read More »
Recommender systems built on metric learning represent each user and item as a point in a shared space and push the distance between a user and the items they like to shrink relative to items they do not like. This…
BIM-CGN: Causal Graph Attention Fixes Metric Learning Recommenders Read More »
Graph neural networks have posted strong results on node classification, link prediction and graph level tasks for years now, but the field’s research energy has overwhelmingly gone into model architecture. New attention mechanisms, new convolution operators, new ways of combining…
EDEN Distills a Directed Graph’s Own Hierarchy Into Better GNN Predictions Read More »