Locating information with uncertainty in fully interconnected networks: The case of nondistributed memory
Bibliographic record
Abstract
Abstract We consider the problem of searching for a piece of information in a fully interconnected computer network (also called a complete network orclique) by exploiting advice about its location from the network nodes. Each node contains a database that “knows” what kind of documents or information are stored in other nodes (e.g., a node could be a Web server that answers queries about documents stored on the Web). The databases in each node, when queried, provide a pointer that leads to the node that contains the information. However, this information is up‐to‐date (or correct) with some bounded probability. While, in principle, one may always locate the information by simply visiting the network nodes in some prescribed ordering, this requires a time complexity in the order of the number of nodes of the network. In this paper, we provide algorithms for locating an information node in the complete communication network, which take advantage ofadvicegiven from network nodes. The nodes may either give correct advice, by pointing directly to the information node, or give wrong advice, by pointing elsewhere. On the lower‐bounds' side, we show that no fixed‐memory (i.e., with memory independent of the network size) deterministic algorithm may locate the information node in a constant (independent of the network size) expected number of steps. Moreover, ifp= ω(1/n) is the probability that a node of ann‐node clique gives correct advice, we show that no algorithm may locate the information node in an expected number of steps less than 1/p−o(1). To study how the expected number of steps is affected by the amount of memory allowed to the algorithms, we give a memoryless randomized algorithm with expected number of steps 4/p+o(1/p) +o(1) and a 1‐bit randomized algorithm requiring on the average at most 2/p+o(1) steps. In addition, in the memoryless case, we also prove a 4/plower bound for the expected number of steps in the case where the nodes giving faulty advice may decide on the content of this advice in any possible way and not merely at random (adversarialfault model). Finally, for the case where faulty nodes behave randomly, we give an optimal, unlimited memory deterministic algorithm with expected number of steps bounded from above by 1/p+o(1/p) + 1. © 2003 Wiley Periodicals, Inc.
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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 | 0.003 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.007 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 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".