Project description
Embracing and addressing uncertainty in graph algorithms
Having a backup plan is always useful. A replacement path is even more essential in graph algorithms, assuming that real-world graphs are vulnerable and subject to nodes and links failures. The assumption that an algorithm has full knowledge of the input data is not always true. In this context, the EU-funded UncertainENV project will investigate the power of randomisation in uncertain environments. Randomisation is a powerful tool in the classic setting of graph algorithms with full knowledge and is often used to simplify the algorithm and speed up its running time. The focus will be on two fundamental areas of graph algorithms with uncertainty: the first relates to dynamic algorithms and the second concerns distributed graph algorithms.
Objective
Much of the research on the foundations of graph algorithms is carried out under the assumption that the algorithm has full knowledge of the input data.
In spite of the theoretical appeal and simplicity of this setting, the assumption that the algorithm has full knowledge does not always hold.
Indeed uncertainty and partial knowledge arise in many settings.
One example is where the data is very large, in which case even reading the entire data once is infeasible, and sampling is required.
Another example is where data changes occur over time (e.g. social networks where information is fluid).
A third example is where processing of the data is distributed over computation nodes, and each node has only local information.
Randomization is a powerful tool in the classic setting of graph algorithms with full knowledge and is often used to simplify the algorithm and to speed-up its running time.
However, physical computers are deterministic machines, and obtaining true randomness can be a hard task to achieve.
Therefore, a central line of research is focused on the derandomization of algorithms that relies on randomness.
The challenge of derandomization also arise in settings where the algorithm has some degree of uncertainty.
In fact, in many cases of uncertainty the challenge and motivation of derandomization is even stronger.
Randomization by itself adds another layer of uncertainty, because different results may be attained in different runs of the algorithm.
In addition, in many cases of uncertainty randomization often comes with additional assumptions on the model itself, and therefore weaken the guarantees of the algorithm.
In this proposal I will investigate the power of randomization in uncertain environments.
I will focus on two fundamental areas of graph algorithms with uncertainty.
The first area relates to dynamic algorithms and the second area concerns distributed graph algorithms.
Fields of science (EuroSciVoc)
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Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
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Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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H2020-EU.1.1. - EXCELLENT SCIENCE - European Research Council (ERC)
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Call for proposal
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(opens in new window) ERC-2018-STG
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69978 Tel Aviv
Israel
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