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Arborescence (graph theory)

This article addresses the topic of Arborescence (graph theory), which has generated great interest in various areas. Arborescence (graph theory) has captured the attention of experts, enthusiasts and the general public, making it relevant to analyze and delve into this topic. Throughout history, Arborescence (graph theory) has played a prominent role in different contexts, influencing social, cultural, political, economic aspects, among others. Therefore, it is imperative to thoroughly explore this topic to understand its impact and relevance today. Through the detailed exploration of Arborescence (graph theory), we seek to provide the reader with a complete and updated vision of this topic, in order to contribute to the enrichment of knowledge and understanding of its importance.

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An instance of an arborescence, where any vertex v has exactly one directed walk from the root vertex to itself.

In graph theory, an arborescence is a directed graph where there exists a vertex r (called the root) such that, for any other vertex v, there is exactly one directed walk from r to v (noting that the root r is unique).[1] An arborescence is thus the directed-graph form of a rooted tree, understood here as an undirected graph.[2][3] An arborescence is also a directed rooted tree in which all edges point away from the root; a number of other equivalent characterizations exist.[4][5]

Every arborescence is a directed acyclic graph (DAG), but not every DAG is an arborescence.

Definition

The term arborescence comes from French.[6] Some authors object to it on grounds that it is cumbersome to spell.[7] There is a large number of synonyms for arborescence in graph theory, including directed rooted tree,[3][7] out-arborescence,[8] out-tree,[9] and even branching being used to denote the same concept.[9] Rooted tree itself has been defined by some authors as a directed graph.[10][11][12]

Further definitions

Furthermore, some authors define an arborescence to be a spanning directed tree of a given digraph.[12] The same can be said about some of its synonyms, especially branching.[13] Other authors use branching to denote a forest of arborescences, with the latter notion defined in broader sense given at beginning of this article,[14][15] but a variation with both notions of the spanning flavor is also encountered.[12]

It's also possible to define a useful notion by reversing all the edges of an arborescence, i.e. making them all point in the direction of the root rather than away from it. Such digraphs are also designated by a variety of terms, such as in-tree[16] or anti-arborescence.[17] W. T. Tutte distinguishes between the two cases by using the phrases arborescence diverging from and arborescence converging to .[18]

The number of rooted trees (or arborescences) with n nodes is given by the sequence:

0, 1, 1, 2, 4, 9, 20, 48, 115, 286, 719, 1842, 4766, 12486, ... (sequence A000081 in the OEIS).

See also

References

  1. ^ Darij Grinberg (2 August 2023). "An introduction to graph theory (Text for Math 530 in Spring 2022 at Drexel University)" (PDF). Darij Grinberg, Ludwig-Maximilians-Universität München. p. 187. Retrieved 2 July 2024. Theorem 5.6.5, Statement A4: For each vertex v ∈ V, the multidigraph D has a unique walk from r to v.
  2. ^ Gordon, Gary (1989). "A greedoid polynomial which distinguishes rooted arborescences". Proceedings of the American Mathematical Society. 107 (2): 287–298. doi:10.1090/S0002-9939-1989-0967486-0.
  3. ^ a b Stanley Gill Williamson (1985). Combinatorics for Computer Science. Courier Dover Publications. p. 288. ISBN 978-0-486-42076-9.
  4. ^ Jean-Claude Fournier (2013). Graphs Theory and Applications: With Exercises and Problems. John Wiley & Sons. pp. 94–95. ISBN 978-1-84821-070-7.
  5. ^ Jean Gallier (2011). Discrete Mathematics. Springer Science & Business Media. pp. 193–194. ISBN 978-1-4419-8046-5.
  6. ^ Per Hage and Frank Harary (1996). Island Networks: Communication, Kinship, and Classification Structures in Oceania. Cambridge University Press. p. 43. ISBN 978-0-521-55232-5.
  7. ^ a b Mehran Mesbahi; Magnus Egerstedt (2010). Graph Theoretic Methods in Multiagent Networks. Princeton University Press. p. 38. ISBN 978-1-4008-3535-5.
  8. ^ Ding-Zhu Du; Ker-I Ko; Xiaodong Hu (2011). Design and Analysis of Approximation Algorithms. Springer Science & Business Media. p. 108. ISBN 978-1-4614-1701-9.
  9. ^ a b Jonathan L. Gross; Jay Yellen; Ping Zhang (2013). Handbook of Graph Theory, Second Edition. CRC Press. p. 116. ISBN 978-1-4398-8018-0.
  10. ^ David Makinson (2012). Sets, Logic and Maths for Computing. Springer Science & Business Media. pp. 167–168. ISBN 978-1-4471-2499-3.
  11. ^ Kenneth Rosen (2011). Discrete Mathematics and Its Applications, 7th edition. McGraw-Hill Science. p. 747. ISBN 978-0-07-338309-5.
  12. ^ a b c Alexander Schrijver (2003). Combinatorial Optimization: Polyhedra and Efficiency. Springer. p. 34. ISBN 3-540-44389-4.
  13. ^ Jørgen Bang-Jensen; Gregory Z. Gutin (2008). Digraphs: Theory, Algorithms and Applications. Springer. p. 339. ISBN 978-1-84800-998-1.
  14. ^ James Evans (1992). Optimization Algorithms for Networks and Graphs, Second Edition. CRC Press. p. 59. ISBN 978-0-8247-8602-1.
  15. ^ Bernhard Korte; Jens Vygen (2012). Combinatorial Optimization: Theory and Algorithms (5th ed.). Springer Science & Business Media. p. 18. ISBN 978-3-642-24488-9.
  16. ^ Kurt Mehlhorn; Peter Sanders (2008). Algorithms and Data Structures: The Basic Toolbox (PDF). Springer Science & Business Media. p. 52. ISBN 978-3-540-77978-0.
  17. ^ Bernhard Korte; Jens Vygen (2012). Combinatorial Optimization: Theory and Algorithms (5th ed.). Springer Science & Business Media. p. 28. ISBN 978-3-642-24488-9.
  18. ^ Tutte, W.T. (2001), Graph Theory, Cambridge University Press, pp. 126–127, ISBN 978-0-521-79489-3
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