Temporal Queries for Dynamic Temporal Forests

Authors Davide Bilò , Luciano Gualà , Stefano Leucci , Guido Proietti , Alessandro Straziota



PDF
Thumbnail PDF

File

LIPIcs.ISAAC.2024.11.pdf
  • Filesize: 1.04 MB
  • 16 pages

Document Identifiers

Author Details

Davide Bilò
  • Department of Information Engineering, Computer Science, and Mathematics, University of L'Aquila, Italy
Luciano Gualà
  • Department of Enterprise Engineering, University of Rome "Tor Vergata", Italy
Stefano Leucci
  • Department of Information Engineering, Computer Science, and Mathematics, University of L'Aquila, Italy
Guido Proietti
  • Department of Information Engineering, Computer Science, and Mathematics, University of L'Aquila, Italy
Alessandro Straziota
  • Department of Enterprise Engineering, University of Rome "Tor Vergata", Italy

Cite As Get BibTex

Davide Bilò, Luciano Gualà, Stefano Leucci, Guido Proietti, and Alessandro Straziota. Temporal Queries for Dynamic Temporal Forests. In 35th International Symposium on Algorithms and Computation (ISAAC 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 322, pp. 11:1-11:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024) https://doi.org/10.4230/LIPIcs.ISAAC.2024.11

Abstract

In a temporal forest each edge has an associated set of time labels that specify the time instants in which the edges are available. A temporal path from vertex u to vertex v in the forest is a selection of a label for each edge in the unique path from u to v, assuming it exists, such that the labels selected for any two consecutive edges are non-decreasing.
We design linear-size data structures that maintain a temporal forest of rooted trees under addition and deletion of both edge labels and singleton vertices, insertion of root-to-node edges, and removal of edges with no labels. Such data structures can answer temporal reachability, earliest arrival, and latest departure queries. All queries and updates are handled in polylogarithmic worst-case time. Our results can be adapted to deal with latencies. More precisely, all the worst-case time bounds are asymptotically unaffected when latencies are uniform. For arbitrary latencies, the update time becomes amortized in the incremental case where only label additions and edge/singleton insertions are allowed as well as in the decremental case in which only label deletions and edge/singleton removals are allowed. 
To the best of our knowledge, the only previously known data structure supporting temporal reachability queries is due to Brito, Albertini, Casteigts, and Travençolo [Social Network Analysis and Mining, 2021], which can handle general temporal graphs, answers queries in logarithmic time in the worst case, but requires an amortized update time that is quadratic in the number of vertices, up to polylogarithmic factors.

Subject Classification

ACM Subject Classification
  • Theory of computation → Data structures design and analysis
  • Theory of computation → Dynamic graph algorithms
Keywords
  • temporal graphs
  • temporal reachability
  • earliest arrival
  • latest departure
  • dynamic forests

Metrics

  • Access Statistics
  • Total Accesses (updated on a weekly basis)
    0
    PDF Downloads

References

  1. Eleni C. Akrida, George B. Mertzios, Sotiris E. Nikoletseas, Christoforos L. Raptopoulos, Paul G. Spirakis, and Viktor Zamaraev. How fast can we reach a target vertex in stochastic temporal graphs? J. Comput. Syst. Sci., 114:65-83, 2020. URL: https://doi.org/10.1016/J.JCSS.2020.05.005.
  2. Eleni C. Akrida, George B. Mertzios, Paul G. Spirakis, and Viktor Zamaraev. Temporal vertex cover with a sliding time window. J. Comput. Syst. Sci., 107:108-123, 2020. URL: https://doi.org/10.1016/J.JCSS.2019.08.002.
  3. Stephen Alstrup, Jacob Holm, Kristian de Lichtenberg, and Mikkel Thorup. Maintaining information in fully dynamic trees with top trees. ACM Trans. Algorithms, 1(2):243-264, 2005. URL: https://doi.org/10.1145/1103963.1103966.
  4. Matthieu Barjon, Arnaud Casteigts, Serge Chaumette, Colette Johnen, and Yessin M. Neggaz. Testing temporal connectivity in sparse dynamic graphs. CoRR, abs/1404.7634, 2014. URL: https://arxiv.org/abs/1404.7634.
  5. Michael A. Bender and Martin Farach-Colton. The LCA problem revisited. In Gaston H. Gonnet, Daniel Panario, and Alfredo Viola, editors, LATIN 2000: Theoretical Informatics, 4th Latin American Symposium, Punta del Este, Uruguay, April 10-14, 2000, Proceedings, volume 1776 of Lecture Notes in Computer Science, pages 88-94. Springer, 2000. URL: https://doi.org/10.1007/10719839_9.
  6. Davide Bilò, Sarel Cohen, Tobias Friedrich, Hans Gawendowicz, Nicolas Klodt, Pascal Lenzner, and George Skretas. Temporal network creation games. In Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, IJCAI 2023, 19th-25th August 2023, Macao, SAR, China, pages 2511-2519. ijcai.org, 2023. URL: https://doi.org/10.24963/IJCAI.2023/279.
  7. Davide Bilò, Gianlorenzo D'Angelo, Luciano Gualà, Stefano Leucci, and Mirko Rossi. Sparse temporal spanners with low stretch. In Shiri Chechik, Gonzalo Navarro, Eva Rotenberg, and Grzegorz Herman, editors, 30th Annual European Symposium on Algorithms, ESA 2022, September 5-9, 2022, Berlin/Potsdam, Germany, volume 244 of LIPIcs, pages 19:1-19:16. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2022. URL: https://doi.org/10.4230/LIPICS.ESA.2022.19.
  8. Davide Bilò, Gianlorenzo D'Angelo, Luciano Gualà, Stefano Leucci, and Mirko Rossi. Blackout-tolerant temporal spanners. J. Comput. Syst. Sci., 141:103495, 2024. URL: https://doi.org/10.1016/J.JCSS.2023.103495.
  9. Luiz F. Afra Brito, Marcelo Keese Albertini, Arnaud Casteigts, and Bruno Augusto Nassif Travençolo. A dynamic data structure for temporal reachability with unsorted contact insertions. Social Network Analysis and Mining, 12, 2021. URL: https://api.semanticscholar.org/CorpusID:231847148.
  10. Arnaud Casteigts, Anne-Sophie Himmel, Hendrik Molter, and Philipp Zschoche. Finding temporal paths under waiting time constraints. Algorithmica, 83(9):2754-2802, 2021. URL: https://doi.org/10.1007/S00453-021-00831-W.
  11. Arnaud Casteigts, Joseph G. Peters, and Jason Schoeters. Temporal cliques admit sparse spanners. J. Comput. Syst. Sci., 121:1-17, 2021. URL: https://doi.org/10.1016/J.JCSS.2021.04.004.
  12. Arnaud Casteigts, Michael Raskin, Malte Renken, and Viktor Zamaraev. Sharp thresholds in random simple temporal graphs. SIAM J. Comput., 53(2):346-388, 2024. URL: https://doi.org/10.1137/22M1511916.
  13. Thomas Erlebach, Frank Kammer, Kelin Luo, Andrej Sajenko, and Jakob T. Spooner. Two moves per time step make a difference. In Christel Baier, Ioannis Chatzigiannakis, Paola Flocchini, and Stefano Leonardi, editors, 46th International Colloquium on Automata, Languages, and Programming, ICALP 2019, July 9-12, 2019, Patras, Greece, volume 132 of LIPIcs, pages 141:1-141:14. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2019. URL: https://doi.org/10.4230/LIPICS.ICALP.2019.141.
  14. Thomas Erlebach and Jakob T. Spooner. Parameterised temporal exploration problems. J. Comput. Syst. Sci., 135:73-88, 2023. URL: https://doi.org/10.1016/J.JCSS.2023.01.003.
  15. Thekla Hamm, Nina Klobas, George B. Mertzios, and Paul G. Spirakis. The complexity of temporal vertex cover in small-degree graphs. In Thirty-Sixth AAAI Conference on Artificial Intelligence, AAAI 2022, Thirty-Fourth Conference on Innovative Applications of Artificial Intelligence, IAAI 2022, The Twelveth Symposium on Educational Advances in Artificial Intelligence, EAAI 2022 Virtual Event, February 22 - March 1, 2022, pages 10193-10201. AAAI Press, 2022. URL: https://doi.org/10.1609/AAAI.V36I9.21259.
  16. Kathrin Hanauer, Monika Henzinger, and Christian Schulz. Recent advances in fully dynamic graph algorithms - A quick reference guide. ACM J. Exp. Algorithmics, 27:1.11:1-1.11:45, 2022. URL: https://doi.org/10.1145/3555806.
  17. Petter Holme. Temporal networks. In Encyclopedia of Social Network Analysis and Mining, pages 2119-2129. Springer, 2014. URL: https://doi.org/10.1007/978-1-4614-6170-8_42.
  18. Shang-En Huang, Dawei Huang, Tsvi Kopelowitz, Seth Pettie, and Mikkel Thorup. Fully dynamic connectivity in o(log n(loglog n)^2) amortized expected time. TheoretiCS, 2, 2023. URL: https://doi.org/10.46298/THEORETICS.23.6.
  19. David Kempe, Jon Kleinberg, and Amit Kumar. Connectivity and inference problems for temporal networks. Journal of Computer and System Sciences, 64(4):820-842, 2002. URL: https://doi.org/10.1006/jcss.2002.1829.
  20. Edward M. McCreight. Priority search trees. SIAM J. Comput., 14(2):257-276, 1985. URL: https://doi.org/10.1137/0214021.
  21. Daniel D. Sleator and Robert Endre Tarjan. A data structure for dynamic trees. Journal of Computer and System Sciences, 26(3):362-391, 1983. URL: https://doi.org/10.1016/0022-0000(83)90006-5.
  22. Huanhuan Wu, James Cheng, Silu Huang, Yiping Ke, Yi Lu, and Yanyan Xu. Path problems in temporal graphs. Proc. VLDB Endow., 7(9):721-732, 2014. URL: https://doi.org/10.14778/2732939.2732945.
Questions / Remarks / Feedback
X

Feedback for Dagstuhl Publishing


Thanks for your feedback!

Feedback submitted

Could not send message

Please try again later or send an E-mail