On Temporal Bipartite Graphs and Their Application in Disease Spread Prediction
Bibliographic record
Abstract
The original temporal bipartite graph is flawed in the context of disease spreading models as it does not account for concepts such as virus incu-bation and recovery periods. In this thesis, a new graph structure, referred to as the improved temporal bipartite graph is introduced with these two concepts incorporated to enhance accuracy in predicting disease spreading. To facilitate arbitrary reachability queries, another concept, the transmission graph, is introduced. It is derived from a temporal bipartite graph based on a series of reachability query evaluation. We distinguish between two types: single-path transmission graph and multi-path trans-mission graph. Based on them, four algorithms are proposed for evaluating reachability queries on a temporal bipartite graph, with a label-based technique used to achieve high efficiency. Both single-path transmission graphs and multi-path transmission graphs are in fact a kind of extension of the reachability query evaluation. By establishing indexes over them, the reachability query evaluation for disease spreading prediction can be very efficiently conducted.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".