Kemeny's constant and Wiener index on trees

On trees of fixed order, we show a direct relation between Kemeny's constant and Wiener index, and provide a new formula of Kemeny's constant from the relation with a combinatorial interpretation. Moreover, the relation simplifies proofs of several known results for extremal trees in terms of Kemeny's constant for random walks on trees. Finally, we provide various families of co-Kemeny's mates, which are two non-isomorphic connected graphs with the same Kemeny's constant, and we also give a necessary condition for a tree to attain maximum Kemeny's constant for trees with fixed diameter

The analysis of increasing trees and other families of trees

9502325T Faculty of Science School of MathematicsAbstract Increasing trees are labelled rooted trees in which labels along any branch from the root appear in increasing order. They have numerous applications in tree representations of permutations, data structures in computer science and probabilistic models in a multitude of problems. We use a generating function approach for the computation of parameters arising from such trees. The generating functions for some parameters are shown to be related to ordinary differential equations. Singularity analysis is then used to analyze several parameters of the trees asymptotically.Various classes of trees are considered. Parameters such as depth and path length for heap ordered trees have been analyzed in [35]. We follow a similar approach to determine grand averages for such trees. The model is that p of the n nodes are labelled at random in ôn p(ways, and the characteristic parameters depend on these labelled nodes. Also, we will attempt to look at the limiting distributions involved. Often, when they are Gaussian, Hwang's quasi power theorem, from [18], can be employed. Spanning tree size and the Wiener index for binary search trees have been computed in [33]. The Wiener index is the sum of all distances between pairs of nodes in a tree. Arelated parameter of interest is the Steiner distance which generalises, to sets of k nodes, the Wiener index (k=2). Furthermore, the distribution of the size of the ancestor-tree and of the induced spanning subtree for random trees is presented in [30]. The same procedure is followed to obtain the Steiner distance for heap ordered trees and for other varieties of increasing trees

On weighted depths in random binary search trees

Following the model introduced by Aguech, Lasmar and Mahmoud [Probab. Engrg. Inform. Sci. 21 (2007) 133-141], the weighted depth of a node in a labelled rooted tree is the sum of all labels on the path connecting the node to the root. We analyze weighted depths of nodes with given labels, the last inserted node, nodes ordered as visited by the depth first search process, the weighted path length and the weighted Wiener index in a random binary search tree. We establish three regimes of nodes depending on whether the second order behaviour of their weighted depths follows from fluctuations of the keys on the path, the depth of the nodes, or both. Finally, we investigate a random distribution function on the unit interval arising as scaling limit for weighted depths of nodes with at most one child

The Minimum Wiener Connector

The Wiener index of a graph is the sum of all pairwise shortest-path distances between its vertices. In this paper we study the novel problem of finding a minimum Wiener connector: given a connected graph $G=(V,E)$ and a set $Q\subseteq V$ of query vertices, find a subgraph of $G$ that connects all query vertices and has minimum Wiener index. We show that The Minimum Wiener Connector admits a polynomial-time (albeit impractical) exact algorithm for the special case where the number of query vertices is bounded. We show that in general the problem is NP-hard, and has no PTAS unless $\mathbf{P} = \mathbf{NP}$. Our main contribution is a constant-factor approximation algorithm running in time $\widetilde{O}(|Q||E|)$. A thorough experimentation on a large variety of real-world graphs confirms that our method returns smaller and denser solutions than other methods, and does so by adding to the query set $Q$ a small number of important vertices (i.e., vertices with high centrality).Comment: Published in Proceedings of the 2015 ACM SIGMOD International Conference on Management of Dat

The center of mass of the ISE and the Wiener index of trees

We derive the distribution of the center of mass $S$ of the integrated superBrownian excursion (ISE) {from} the asymptotic distribution of the Wiener index for simple trees. Equivalently, this is the distribution of the integral of a Brownian snake. A recursion formula for the moments and asymptotics for moments and tail probabilities are derived.Comment: 11 page