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Record W2463954311 · doi:10.1145/3280823

Deterministic Graph Exploration with Advice

2018· preprint· en· W2463954311 on OpenAlexafffund
Barun Gorain, Andrzej Pelc

Bibliographic record

VenueACM Transactions on Algorithms · 2018
Typepreprint
Languageen
FieldComputer Science
TopicOptimization and Search Problems
Canadian institutionsUniversité du Québec en Outaouais
FundersNatural Sciences and Engineering Research Council of CanadaUniversité du Québec en Outaouais
KeywordsOracleAdvice (programming)GraphComputer scienceTheoretical computer scienceTime complexityCombinatoricsA priori and a posterioriNode (physics)Discrete mathematicsMathematicsAlgorithm

Abstract

fetched live from OpenAlex

We consider the fundamental task of graph exploration. An n -node graph has unlabeled nodes, and all ports at any node of degree d are arbitrarily numbered 0,…, d −1. A mobile agent, initially situated at some starting node v , has to visit all nodes and stop. The time of the exploration is the number of edge traversals. We consider the problem of how much knowledge the agent has to have a priori , to explore the graph in a given time, using a deterministic algorithm. Following the paradigm of algorithms with advice , this a priori information (advice) is provided to the agent by an oracle , in the form of a binary string, whose length is called the size of advice . We consider two types of oracles. The instance oracle knows the entire instance of the exploration problem, i.e., the port-numbered map of the graph and the starting node of the agent in this map. The map oracle knows the port-numbered map of the graph but does not know the starting node of the agent. What is the minimum size of advice that must be given to the agent by each of these oracles, so that the agent explores the graph in a given time? We first determine the minimum size of advice to achieve exploration in polynomial time. We prove that some advice of size log log log n − c , for any constant c , is sufficient for polynomial exploration, and that no advice of size log log log n −ϕ ( n ), where ϕ is any function diverging to infinity, can help to do this. These results hold both for the instance and for the map oracles. On the other side of the spectrum, when advice is large, there are two natural time thresholds: Θ ( n 2 ) for a map oracle, and Θ ( n ) for an instance oracle. This is because, in both cases, these time benchmarks can be achieved with sufficiently large advice (advice of size O ( n log n ) suffices). We show that, with a map oracle, time Θ ( n 2 ) cannot be improved in general, regardless of the size of advice. What is then the smallest advice to achieve time Θ ( n 2 ) with a map oracle? We show that this smallest size of advice is larger than n δ , for any δ < 1/3. For large advice, the situation changes significantly when we allow an instance oracle instead of a map oracle. In this case, advice of size O ( n log n ) is enough to achieve time O ( n ). Is such a large advice needed to achieve linear time? We answer this question affirmatively. Indeed, we show more: with any advice of size o ( n log n ), the time of exploration must be at least n ϵ , for any ϵ < 2, and with any advice of size O ( n ), the time must be Ω( n 2 ). We finally look at Hamiltonian graphs, as for them it is possible to achieve the absolutely optimal exploration time n −1, when sufficiently large advice (of size o ( n log n )) is given by an instance oracle. We show that a map oracle cannot achieve this: regardless of the size of advice, the time of exploration must be Ω( n 2 ), for some Hamiltonian graphs. However, even for the instance oracle, with advice of size o ( n log n ), optimal time n −1 cannot be achieved: Indeed, we show that the time of exploration with such advice must sometimes exceed the optimal time n −1 by a summand n ϵ , for any ϵ < 1.

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.017
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.005
Open science0.0020.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.044
GPT teacher head0.291
Teacher spread0.247 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2018
Admission routes2
Has abstractyes

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