arXiv Machine Learning
NodeGround: A Node Classification Benchmark in the Graph Foundation Model Era
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
Related stories
arXiv Machine Learning
Jul 21
Node4All: Learning Node Representation Beyond Datasets
arXiv:2607. 17272v1 Announce Type: new Abstract: Node representation learning has advanced rapidly, yet most existing methods rely on per-dataset training and hyperparameter tuning.
arXiv Machine Learning
3d ago
Message Passing Does More with Less for In-Context Learning on Graphs
arXiv:2609.37057v1 Announce Type: new Abstract: Achieving strong performance with graph neural networks (GNNs) typically requires training and hyperparameter tuning for each dataset, incurring repeat...
arXiv AI
Jun 11
GILT: An LLM-Free, Tuning-Free Graph Foundational Model for In-Context Learning
arXiv:2510. 04567v3 Announce Type: replace-cross Abstract: Graph Neural Networks (GNNs) are powerful tools for processing relational data but often struggle to generalize to unseen graphs, giving rise to the development of Graph Foundational Models (GFMs).
arXiv Machine Learning
Jun 11
GraphInfer-Bench: Benchmarking LLM's Inference Capability on Graphs
arXiv:2606. 11562v1 Announce Type: new Abstract: Graph analysis underlies many applications whose answers cannot be looked up in a single record or retrieved along a path: laundering rings, drug repurposing, user preference, and scientific theme are all inferred from a node together with its neighbourhood.
arXiv Machine Learning
Jun 2
OgBench: A Framework for Evaluating Graph Neural Networks on Omics Data
arXiv:2605. 15511v2 Announce Type: replace Abstract: Graph Neural Networks (GNNs) have become the dominant framework for inductive graph-level learning.
arXiv Machine Learning
Aug 21
GraphPFN: A Prior-Data Fitted Graph Foundation Model
arXiv:2509. 21489v4 Announce Type: replace Abstract: Graph foundation models face several fundamental challenges including transferability across diverse domains and data scarcity, which calls into question the very feasibility of creating such models.