Multistage s-t Path: Confronting Similarity with Dissimilarity in Temporal Graphs

Authors Till Fluschnik , Rolf Niedermeier , Carsten Schubert, Philipp Zschoche



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Till Fluschnik
  • Technische Universität Berlin, Algorithmics and Computational Complexity, Germany
Rolf Niedermeier
  • Technische Universität Berlin, Algorithmics and Computational Complexity, Germany
Carsten Schubert
  • Technische Universität Berlin, Algorithmics and Computational Complexity, Germany
Philipp Zschoche
  • Technische Universität Berlin, Algorithmics and Computational Complexity, Germany

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Till Fluschnik, Rolf Niedermeier, Carsten Schubert, and Philipp Zschoche. Multistage s-t Path: Confronting Similarity with Dissimilarity in Temporal Graphs. In 31st International Symposium on Algorithms and Computation (ISAAC 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 181, pp. 43:1-43:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020) https://doi.org/10.4230/LIPIcs.ISAAC.2020.43

Abstract

Addressing a quest by Gupta et al. [ICALP'14], we provide a first, comprehensive study of finding a short s-t path in the multistage graph model, referred to as the Multistage s-t Path problem. Herein, given a sequence of graphs over the same vertex set but changing edge sets, the task is to find short s-t paths in each graph ("snapshot") such that in the found path sequence the consecutive s-t paths are "similar". We measure similarity by the size of the symmetric difference of either the vertex set (vertex-similarity) or the edge set (edge-similarity) of any two consecutive paths. We prove that these two variants of Multistage s-t Path are already NP-hard for an input sequence of only two graphs and maximum vertex degree four. Motivated by this fact and natural applications of this scenario e.g. in traffic route planning, we perform a parameterized complexity analysis. Among other results, for both variants, vertex- and edge-similarity, we prove parameterized hardness (W[1]-hardness) regarding the parameter path length (solution size) for both variants, vertex- and edge-similarity. As a further conceptual study, we then modify the multistage model by asking for dissimilar consecutive paths. One of our main technical results (employing so-called representative sets known from non-temporal settings) is that dissimilarity allows for fixed-parameter tractability for the parameter solution size, contrasting the W[1]-hardness of the corresponding similarity case. We also provide partially positive results concerning efficient and effective data reduction (kernelization).

Subject Classification

ACM Subject Classification
  • Theory of computation → Parameterized complexity and exact algorithms
  • Theory of computation → Graph algorithms analysis
Keywords
  • Temporal graphs
  • shortest paths
  • consecutive similarity
  • consecutive dissimilarity
  • parameterized complexity
  • kernelization
  • representative sets in temporal graphs

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