An advanced deep learning framework for simulating information propagation dynamics
Why this work is in the frame
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Bibliographic record
Abstract
The warehouse model, based on differential equations, has been widely employed in the field of network information propagation for an extended period. Numerous studies have revolved around the construction, fitting and simulation of these models. However, there has not been a universal and efficient fitting method applicable to all warehouse models in the realm of information propagation, mainly due to the often challenging nature of solving differential equations in practical scenarios. In this article, we introduce a deep learning-based framework for simulating information propagation dynamics. This framework is grounded in a model that embeds a physical neural network and can be employed for fitting data from sentiment analysis platforms. We apply our framework to classic information propagation dynamic models, achieving favorable fitting results and consistent experimental outcomes, underscoring the advancement of our approach.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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