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Conference Papers Year : 2021

Temporal Matching on Geometric Graph Data

Abstract

Temporal graphs are the modeling of pairwise and historical interaction in recordings of a dataset. A temporal matching formalizes the planning of pair working sessions of a required duration. We depict algorithms finding temporal matchings maximizing the total workload, by an exact algorithm and an approximation. The exact algorithm is a dynamic programming solving the general case in O*((γ + 1) n) time, where n is the number of vertices, γ represents the desired duration of each pair working session, and O* only focuses on exponential factors. When the input data is embedded in an Euclidean space, called geometric data, our approximation is based on a new notion of temporal velocity. We revise a known notion of static density [van Leeuwen, 2009] and result in a polynomial time approximation scheme for temporal geometric graphs of bounded density. We confront our implementations to known opensource implementation.
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Dates and versions

hal-03095671 , version 1 (04-01-2021)
hal-03095671 , version 2 (20-01-2021)

Identifiers

  • HAL Id : hal-03095671 , version 2

Cite

Timothe Picavet, Ngoc-Trung Nguyen, Binh-Minh Bui-Xuan. Temporal Matching on Geometric Graph Data. CIAC 2021 - 12th International Conference on Algorithms and Complexity, May 2021, Larnaca, Cyprus. ⟨hal-03095671v2⟩
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