Smooth Transitions in Graph Self-Supervision: Mitigating Feature Twist Across Abstraction Levels
Bibliographic record
Abstract
Self-supervised learning has emerged as a powerful paradigm for graph representation learning, enabling models to leverage structural and feature information without manual labels. Despite its success, the geometric challenges posed by transitions across abstraction levels, including node, proximity, cluster, and graph granularities, remain underexplored. This paper introduces a novel self-supervised contrastive learning framework and investigates the evolution of intrinsic dimensions during these transitions. Leveraging a pretraining and finetuning protocol, we highlight a critical challenge termed Feature Twist, characterized by abrupt geometric distortions in the latent space, leading to performance degradation in downstream tasks. To address this, we propose a robust filtering mechanism that smooths transitions by mitigating these distortions. Experimental evaluations on benchmark datasets demonstrate the competitive advantages of our approach across common downstream tasks, including node classification, node clustering, and link prediction. Our findings underscore the importance of geometric considerations in self-supervised graph representation learning.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".