A Mean-Field Team Approach to Minimize the Spread of Infection in a\n Network
Bibliographic record
Abstract
In this paper, a stochastic dynamic control strategy is presented to prevent\nthe spread of an infection over a homogeneous network. The infectious process\nis persistent, i.e., it continues to contaminate the network once it is\nestablished. It is assumed that there is a finite set of network management\noptions available such as degrees of nodes and promotional plans to minimize\nthe number of infected nodes while taking the implementation cost into account.\nThe network is modeled by an exchangeable controlled Markov chain, whose\ntransition probability matrices depend on three parameters: the selected\nnetwork management option, the state of the infectious process, and the\nempirical distribution of infected nodes (with not necessarily a linear\ndependence). Borrowing some techniques from mean-field team theory the optimal\nstrategy is obtained for any finite number of nodes using dynamic programming\ndecomposition and the convolution of some binomial probability mass functions.\nFor infinite-population networks, the optimal solution is described by a\nBellman equation. It is shown that the infinite-population strategy is a\nmeaningful sub-optimal solution for finite-population networks if a certain\ncondition holds. The theoretical results are verified by an example of rumor\ncontrol in social networks.\n
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".