Deep Dynamic Mixed Membership Stochastic Blockmodel
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
Latent community models are successful at statistically modeling network data by assigning network entities to communities and modelling entity relations as the relations of their communities. In this paper, we describe the limitation of these models in inferring relations between two communities when the entity relations between these communities are unobserved. We propose a solution to this problem by factorizing the community relations matrix into two community feature matrices, thereby adding a dependency between community relations. We introduce the deep dynamic mixed membership stochastic blockmodel based network (DDBN) to demonstrate the feasibility of such an approach. Our model marries the mixed membership stochastic blockmodel (MMSB) with deep neural networks for rich feature extraction and introduces a temporal dependency in latent features using a long short-term memory unit for dynamic network modeling. We evaluate our model on the link prediction task in static and dynamic networks and find that our model achieves comparable results with state-of-the-art methods.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.006 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.003 |
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