Deep Learning of Latent Edge Types from Relational Data
Bibliographic record
Abstract
Many relational datasets, including relational databases, feature links of different types (e.g., actors act in movies, users rate movies), known as multi-relational, heterogeneous, or multi-layer graphs. Edge types/graph layers are often incompletely labeled. For example, IMDb lists Tom Cruise as a cast member of Mission Impossible, but not as its star. Inferring latent layers is useful for relational prediction tasks (e.g. predict Tom Cruise’s salary or his presence in other movies). This paper describes a Latent Layer Generative Framework - LLGF that extends graph encoder-decoder architectures to include latent layers. The decoder treats the observed edge type signalas a linear combination of latent layers. The encoder infers parallel node representations, one for each latent layer. We evaluate our proposed framework on six benchmark graph learning datasets. Qualitative evidence indicates that LLGF recovers ground truth layers well. For link prediction as a downstream task, we find that extending Variational Graph Auto-Encoders with LLGF increases link prediction accuracy compared to state-of-theart graph Variational Auto-Encoders (up to 6% AUC depending on the dataset).
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".