Exploring Attention and Self-Supervised Learning Mechanism for Graph Similarity Learning
Bibliographic record
Abstract
Graph similarity estimation is a challenging task due to the complex graph structures. Though important and well-studied, three critical aspects are yet to be fully handled in a unified framework: 1) how to learn richer cross-graph interactions from a pairwise node perspective; 2) how to map the similarity matrix into a similarity score by exploiting the inherent structure in the similarity matrix; and 3) how to establish a self-supervised learning mechanism for graph similarity learning. To solve these issues, we explore multiple attention and self-supervised mechanisms for graph similarity learning in this work. More specifically, we propose a unified self-supervised nodewise attention-guided graph similarity learning framework (SNA-GSL) involving: 1) a correlation-guided contrastive learning for capturing valuable node embeddings and 2) a graph similarity learning for predicting similarity scores with multiple proposed attention mechanisms. Extensive experimental results on graph-graph regression task and graph classification task demonstrate that the proposed SNA-GSL performs favorably against state-of-the-art methods. Moreover, the remarkable achievement of our model in the graph classification task is a clear indication of its exceptional generalization capabilities. The code is available at https://github.com/IntelliDAL/Graph/SNA-GSL.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".