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Facility Location in Evolving Metrics

By David Eisenstat, Claire Mathieu and Nicolas Schabanel


International audienceUnderstanding the dynamics of evolving social or infrastructure networks is a challenge in applied areas such as epidemiology, viral marketing, and urban planning. During the past decade, data has been collected on such networks but has yet to be analyzed fully. We propose to use information on the dynamics of the data to find stable partitions of the network into groups. For that purpose, we introduce a time-dependent, dynamic version of the facility location problem, which includes a switching cost when a client's assignment changes from one facility to another. This might provide a better representation of an evolving network, emphasizing the abrupt change of relationships between subjects rather than the continuous evolution of the underlying network. We show for some realistic examples that this model yields better hypotheses than its counterpart without switching costs, where each snapshot can be optimized independently. For our model, we present an $O(\log nT)$-approximation algorithm and a matching hardness result, where $n$ is the number of clients and $T$ is the number of timesteps. We also give another algorithm with approximation ratio $O(\log nT)$ for a variant model where the decision to open a facility is made independently at each timestep

Topics: [ INFO.INFO-DS ] Computer Science [cs]/Data Structures and Algorithms [cs.DS], [ INFO.INFO-SI ] Computer Science [cs]/Social and Information Networks [cs.SI]
Publisher: HAL CCSD
Year: 2014
OAI identifier: oai:HAL:hal-00984375v1
Provided by: Hal-Diderot

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