An LLM-guided framework for link prediction in homogeneous graphs
Bibliographic record
Abstract
Abstract As social networks grow, link prediction has become vital in network analysis, estimating the likelihood of connections between unconnected nodes based on similarity scores. This study explores the intersection of Large Language Models (LLMs) and Graph Learning, with a particular focus on Link Prediction tasks on Homogeneous Networks, where we are using LLMs to analyze social network structures and predict missing links. There have been several studies that leveraged LLMs for Knowledge Graphs, Heterogeneous Graphs, and Text-Attributed Graphs. However, leveraging LLMs for Homogeneous Graphs with no textual information is still an understudied area, which is what we aimed to explore. We developed a framework that leverages LLMs for link prediction tasks requiring no textual information with different learning strategies. Our results demonstrate improvement in model performance for predicting missing links, especially when provided with few examples or fine-tuned on the domain-specific datasets, achieving results on par with state-of-the-art results, even with no fine-tuning.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".