The 1st International Workshop on Graph Foundation Models (GFM)
Bibliographic record
Abstract
Foundation models such as GPT-4 for natural language processing (NLP), Flamingo for computer vision (CV), have set new benchmarks in AI by delivering state-of-the-art results across various tasks with minimal task-specific data. Despite their success, the application of these models to the graph domain is challenging due to the relational nature of graph-structured data. To address this gap, we propose the Graph Foundation Model (GFM) Workshop, the first workshop for GFMs, dedicated to exploring the adaptation and development of foundation models specifically designed for graph data. The GFM workshop focuses on two critical questions: (1) How can the underlying capabilities of existing foundation models be effectively applied to graph data? (2) What foundational principles should guide the creation of models tailored to the graph domain? Through a curated set of panel sections, keynote talks, and paper presentations, our workshop intends to catalyze innovative approaches and theoretical frameworks for Graph Foundation Models (GFMs). We target a broad audience, encompassing researchers, practitioners, and students, and aim to lay the groundwork for the next wave of breakthroughs in integrating graph data with foundation models.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.015 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.021 | 0.007 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".