Research explainers on graph neural networks — GCNs, GATs, message passing, and graph-based representation learning.
A developer wants a language model to answer questions about a network. Is node 14 connected to node 3. What is the shortest route between two depots. Does this dependency graph have a valid build order. The edges go into…
In-Context Learning on Graph Tasks, Tested and Checked Read More »
A fraud team has a transaction graph with a few million accounts. Almost all of them are ordinary. A few hundred belong to a ring that launders money through layered transfers. When a graph neural network classifies those accounts, each…
CL3E-GNN and the Imbalanced Node Classification Problem Read More »
A volunteer lies in a scanner at one of the Human Connectome Project sites. A screen flashes a cue, and for the next ten seconds or so they tap fingers, listen to a story, or hold a picture of a…
ASFFNet Decodes Brain States From Shorter fMRI Windows Read More »
A new paper lands in a citation database with a title, an abstract and a reference list. Nobody has labelled its field yet. A text classifier would read the abstract and guess. A graph neural network does something else. It…
Graph Neural Networks Mathematics Explained in Depth Read More »
A grading crew on an Alabama highway project trusts a stream of centimeter level corrections to steer its machines. Those corrections come from a statewide network of permanent GNSS reference stations that the Department of Transportation owns and maintains. If…
Statewide CORS Networks as a GNSS Spoofing Sensor Read More »
Graph pattern matching asks a deceptively simple question. Given a small pattern graph, maybe a triangle, maybe a four node clique, find every place in a much larger data graph where that exact shape of connections appears. It shows up…
PGMiner Tackles the Three Ways GPUs Waste Work on Graphs Read More »
Most machine learning arguments are about the model. This one is about the starting line. A team at the University of Southern California took a leading system for completing uncertain knowledge graphs, changed nothing about how it scores facts, and…
QUEST Fixes Where Uncertain Knowledge Graph Models Start Read More »
Graph neural networks look deceptively sturdy on paper. Stack a few convolution layers, pool the node embeddings, feed the result to a classifier, and you get strong numbers on clean benchmark data. The trouble starts once the input graph stops…
HPGRL: How Hierarchical Prototypes Make Graph Classification Resist Noise Read More »
A materials scientist sits down with a fresh stack of papers and already knows the stack is a rounding error. The field now publishes more abstracts in a year than any one person could read in a decade of weekends.…
How LLMs and Concept Graphs Predict New Materials Research Read More »
For half a century, simulating how atoms move has followed the same ritual. Work out the force on every atom, nudge each one a tiny step, then do it again, and again, millions of times over, with each step covering…
Force Free Molecular Dynamics With Equivariant Networks Read More »