Graph Learning

Curved Space, Straighter Retrieval: Why Graph RAG Needs Geometry Retrieval looks simple until the wrong thing keeps showing up. A company builds a graph model over products, papers, suppliers, users, or transactions. The model performs reasonably well inside familiar territory. Then the data shifts. New products appear. A new research domain enters the citation graph. A social platform changes user behavior. The model’s internal knowledge, frozen inside parameters, starts behaving like yesterday’s org chart: technically structured, operationally stale. ...

A hospital changes its treatment protocol. Another keeps the old one. A third removes an approval step that had quietly influenced several downstream decisions. Their datasets now disagree. The usual federated-learning instinct is to treat that disagreement as a problem: smooth it, average it, or design an aggregation rule robust enough to survive it. In causal discovery, however, some disagreements contain precisely the information the global model lacks. Removing a local dependency can expose a previously hidden causal pattern. A policy difference that looks like statistical inconvenience may function as an accidental experiment. ...

Benchmarks Are Where Models Stop Being Inspirational Benchmarks are not glamorous. They are where models go after the demo video, after the conference slide, and after the sentence “this generalizes beautifully” has done its little dance in front of investors. Graph learning badly needs that room. For years, graph machine learning has been evaluated on comfortable territory: molecular graphs, citation networks, small academic datasets, and carefully packaged tasks that are useful but narrow. That helped the field grow. It also created a quiet distortion. A model could look impressive while never having to deal with a social network that changes over time, a circuit whose tiny structural error destroys correctness, a SAT instance where solver choice matters, or a weather graph where the planet is inconveniently spherical. ...

Tools are where many agent demos quietly become invoices. A multi-step LLM agent may look intelligent because it reasons, acts, observes, and repeats. Under the hood, though, it often pays the model to decide every small next move: search here, load that node, look around, check valid actions, fill this argument, try again. Some of those decisions need judgement. Others are basically muscle memory wearing a lab coat. ...

TL;DR for operators Labels are expensive. That is the clean business problem behind this paper. In healthcare, credit review, fraud triage, and scientific classification, organisations often have many observations and too few trusted labels. Semi-supervised learning tries to stretch those scarce labels across the structure of the data rather than pretending every missing label is merely a procurement problem with a nicer dashboard. ...

TL;DR for operators Fraud teams already know the problem: the suspicious review, shop, seller, or account is rarely suspicious in isolation. The useful evidence is scattered across neighbours — same user, same product, same rating pattern, same time window, same commercial ecosystem. The less useful evidence is also scattered there. At scale, that second pile is larger. How inconvenient. ...