SMA-GNN: A Symbol-Aware Graph Neural Network for Signed Link Prediction in Recommender Systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Recommender Systems (RS) play a critical role in enhancing user experiences across online platforms by modeling user-item interactions as bipartite graphs. Predicting signed links in such graphs remains challenging due to the sparsity and complexity of sign distributions and the limitations of traditional methods like matrix factorization and Graph Convolutional Networks (GCNs), which often fail to capture the intricate local topological and sign-based patterns essential for accurate predictions. To address these challenges, we propose SMA-GNN, a framework specifically designed for signed link prediction in bipartite graphs. SMA-GNN combines Local Subgraph Extraction, Two-Anchor Distance Labeling (TADL), and a Symbol-aware Multi-head Attention Mechanism to enhance predictive capability and interpretability. By extracting a closed local subgraph around the target link, our method captures relevant topological and sign contexts. TADL refines this by assigning unique structural labels to nodes based on their proximity to anchor nodes, encapsulating roles and relationships. The symbol-aware attention mechanism integrates edge sign information into the message-passing process, generating highly discriminative subgraph embeddings. Experiments on benchmark datasets show that SMA-GNN outperforms global embedding methods in prediction accuracy and provides deeper insights into user-item interactions, enabling more precise and personalized recommendations. Our code is avilable at https://github.com/xiaohuzidefeijian/SMAGNN/tree/master
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it