Information and Computation· 2026Q2
Distributed graph algorithms with predictions
- 1citations
- Q2SCImago
- 2026year
Short summary
This paper introduces deterministic distributed graph algorithms that leverage potentially incorrect predictions to speed up computation in synchronous systems, while ensuring performance doesn't degrade significantly if predictions are bad.
AI-generated from the title and abstract; the full text is not read.
Key points
- Introduces deterministic distributed graph algorithms using predictions in synchronous systems.
- Algorithms aim for speedup with good predictions and robustness against bad ones.
- Adapts concepts from general algorithms with predictions (error measures, robustness) to the distributed graph setting.
- Presents a framework for evaluating these algorithms and methods for transforming existing ones.
- Demonstrates the approach with algorithms for the Maximal Independent Set problem.
AI-generated from the title and abstract; the full text is not read.
Abstract
We initiate the study of deterministic distributed graph algorithms with predictions in synchronous message passing systems. The process at each node in the graph is given a prediction, some extra information about the problem instance, which may be incorrect and which it may use to help obtain a solution. The goal is to develop algorithms that are faster when the information is mostly good and are never much slower than algorithms without predictions, even when the information is mostly bad. Concepts from the more general area of algorithms with predictions, such as error measures, consistency, robustness, and smoothness, are adapted to distributed graph algorithms with predictions. We present a framework for evaluating distributed graph algorithms with predictions and methods for transforming existing algorithms without predictions to effectively use this extra information. Our approach is illustrated by developing algorithms with predictions for the Maximal Independent Set problem.
The authors' abstract, as published at the source. Information and Computation, 2026 · DOI ↗
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Field: Computational Theory and Mathematics
Computational Theory and MathematicsComputer Science