9,152 research outputs found
Five results on maximizing topological indices in graphs
In this paper, we prove a collection of results on graphical indices. We
determine the extremal graphs attaining the maximal generalized Wiener index
(e.g. the hyper-Wiener index) among all graphs with given matching number or
independence number. This generalizes some work of Dankelmann, as well as some
work of Chung. We also show alternative proofs for two recents results on
maximizing the Wiener index and external Wiener index by deriving it from
earlier results. We end with proving two conjectures. We prove that the maximum
for the difference of the Wiener index and the eccentricity is attained by the
path if the order is at least and that the maximum weighted Szeged
index of graphs of given order is attained by the balanced complete bipartite
graphs.Comment: 13 pages, 4 figure
The Wiener polarity index of benzenoid systems and nanotubes
In this paper, we consider a molecular descriptor called the Wiener polarity
index, which is defined as the number of unordered pairs of vertices at
distance three in a graph. Molecular descriptors play a fundamental role in
chemistry, materials engineering, and in drug design since they can be
correlated with a large number of physico-chemical properties of molecules. As
the main result, we develop a method for computing the Wiener polarity index
for two basic and most commonly studied families of molecular graphs, benzenoid
systems and carbon nanotubes. The obtained method is then used to find a closed
formula for the Wiener polarity index of any benzenoid system. Moreover, we
also compute this index for zig-zag and armchair nanotubes
Persisting randomness in randomly growing discrete structures: graphs and search trees
The successive discrete structures generated by a sequential algorithm from
random input constitute a Markov chain that may exhibit long term dependence on
its first few input values. Using examples from random graph theory and search
algorithms we show how such persistence of randomness can be detected and
quantified with techniques from discrete potential theory. We also show that
this approach can be used to obtain strong limit theorems in cases where
previously only distributional convergence was known.Comment: Official journal fil
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