Bibliographic record
Abstract
In a peer-to-peer complex environment, information is permanently diffused. Such an environment can be modeled as a graph, where there are flows of information. The interest of such modeling is that (1) one can describe the exchanges through time from an initial state of the network, (2) the description can be used through the fit of a real-world case and to perform further forecasts, and (3) it can be used to trace information through time. In this paper, we review the methodology for describing diffusion processes on a network in the context of exchange of information in a crypto (Bitcoin) peer-to-peer network. Necessary definitions are posed, and the diffusion equation is derived by considering two different types of Laplacian operators. Equilibrium conditions are discussed, and analytical solutions are derived, particularly in the context of a directed graph, which constitutes the main innovation of this paper. Further innovations follow as the inclusion of boundary conditions, as well as the implementation of delay in the diffusion equation, followed by a discussion when doing approximations useful for the implementation. Numerous numerical simulations additionally illustrate the theory developed all along the paper. Specifically, we validated, through simple examples, the derived analytic solutions, and implemented them in more sophisticated graphs, e.g., the ring graph, particularly important in crypto peer-to-peer networks. As a conclusion for this article, we further developed a theory useful for fitting purposes in order to gain more information on its diffusivity, and through a modeling which the scientific community is aware of.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".