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Evidence record 10203 · automatically gathered

Surprisingly Simple and Effective Multi-Domain Graph Foundation Model through Graph-to-Table Alignment

Graph Foundation Models (GFMs) have emerged as a promising paradigm for learning transferable representations across diverse graph domains. Recent advancements in GFMs have been largely dominated by two paradigms: Graph Neural Network and Large Language Model (LLM) based methods. However, these methods often face a fundamental dilemma between training with limited data and a heavy reliance on textual attributes. Tabular foundation models (TFMs) offer a potential alternative, as node features and

Record details

Published: 13 July 2026
Source: arXiv cs.LG
Category: Research
Topics: Safety & alignment
Retrieved: 14 July 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

Shared topic: Safety & alignment

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How to cite this record

ethics.ai (13 July 2026), “Surprisingly Simple and Effective Multi-Domain Graph Foundation Model through Graph-to-Table Alignment,” evidence record 10203, https://ethics.ai/record/10203 (originally published by arXiv cs.LG).

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