The paper introduces a method that transforms knowledge graph facts into a fixed vocabulary representation, where each fact becomes a node linked to its subject, object, and relation type via six meta-relations. Using this representation, standard GNNs (e.g., GAT, GINE, GraphSAGE, R-GCN) trained on a single small graph can achieve zero‑shot link prediction on 40 inductive benchmarks, matching the performance of specialized foundation models like ULTRA. The approach also generalizes to relational databases, enabling foreign‑key prediction without cell values or schema text, and the authors provide code, checkpoints, and evaluation tools for all benchmarks.
arXiv:2606. 18001v1 Announce Type: new Abstract: Knowledge graph (KG) foundation models (KGFMs) are zero-shot generalizers: trained once, they can predict links on unseen graphs without retraining.
The paper investigates whether prior‑data‑fitted networks (PFNs) can be extended to graph‑based relational models. The authors pretrain a message‑passing foundation model, Griffin, on large synthetic relational databases and evaluate its frozen encoder on 16 real‑world relational classification tasks. A curriculum that starts with schema‑guided synthetic data and then uses schema‑agnostic data yields the best in‑context performance, outperforming real‑data‑only pretraining and other mixtures.
Prior-data fitted networks (PFNs) show that pretraining on large populations of synthetic tasks can enable generalization to unseen datasets with little or no downstream fine-tuning. This paradigm is...
arXiv:2609.12552v1 Announce Type: new Abstract: Open-vocabulary detection accepts any class list at inference, and promptable segmentation returns regions without class names: the taxonomy has left t...
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).
The paper introduces LitEm, a neural regression model that allows transductive knowledge graph embedding models to predict numerical attributes. LitEm achieves top or near‑top performance on most attributes across datasets such as FB15K‑237, YAGO15K, DB15K, and Mutagenesis. A co‑training framework further improves link prediction for bilinear models while enabling them to predict numerical attributes, demonstrating literal‑aware encoding of attribute information.
arXiv:2605.10247v2 Announce Type: replace Abstract: Applying Large Language Models (LLMs) to graph-structured data usually involves multi-step pipelines in which textual node attributes are compresse...
arXiv:2610.06694v2 Announce Type: replace Abstract: Graph foundation models aim to transfer across graphs, feature spaces, relational schemas, and prediction tasks, yet existing approaches typically...
arXiv:2607. 11374v1 Announce Type: new Abstract: Graph Foundation Models (GFMs) have emerged as a promising paradigm for learning transferable representations across diverse graph domains.
arXiv:2605. 15511v2 Announce Type: replace Abstract: Graph Neural Networks (GNNs) have become the dominant framework for inductive graph-level learning.
Graph-to-table systems combine three design choices: an encoder that maps relational evidence to feature rows, a context constructor that selects labelled examples, and a tabular decoder. We investiga...
arXiv:2606. 16509v1 Announce Type: new Abstract: Link prediction in knowledge graphs fundamentally depends on the quality of learned embeddings for entities and relations.