Back

Independent Sets of Random Trees and Sparse Random Graphs.

  • Published In: Journal of Graph Theory, 2025, v. 109, n. 3. P. 294 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Heilman, Steven 3 of 3

Abstract

An independent set of size k in a finite undirected graph G is a set of k vertices of the graph, no two of which are connected by an edge. Let xk(G) be the number of independent sets of size k in the graph G and let α(G)=max{k≥0:xk(G)≠0}. In 1987, Alavi, Malde, Schwenk, and Erdős asked if the independent set sequence x0(G),x1(G),...,xα(G)(G) of a tree is unimodal (the sequence goes up and then down). This problem is still open. In 2006, Levit and Mandrescu showed that the last third of the independent set sequence of a tree is decreasing. We show that the first 46.8% of the independent set sequence of a random tree is increasing with (exponentially) high probability as the number of vertices goes to infinity. So, the question of Alavi, Malde, Schwenk, and Erdős is "four‐fifths true," with high probability. We also show unimodality of the independent set sequence of Erdős–Rényi random graphs, when the expected degree of a single vertex is large (with [exponentially] high probability as the number of vertices in the graph goes to infinity, except for a small region near the mode). A weaker result is shown for random regular graphs. The structure of independent sets of size k as k varies is of interest in probability, statistical physics, combinatorics, and computer science. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Journal of Graph Theory. 2025/07, Vol. 109, Issue 3, p294
  • Document Type:Article
  • Subject Area:Mathematics
  • Publication Date:2025
  • ISSN:0364-9024
  • DOI:10.1002/jgt.23225
  • Accession Number:185153489
  • Copyright Statement:Copyright of Journal of Graph Theory is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Looking to go deeper into this topic? Look for more articles on EBSCOhost.