79 research outputs found
Eccentric connectivity index
The eccentric connectivity index is a novel distance--based molecular
structure descriptor that was recently used for mathematical modeling of
biological activities of diverse nature. It is defined as \,, where and
denote the vertex degree and eccentricity of \,, respectively. We survey
some mathematical properties of this index and furthermore support the use of
eccentric connectivity index as topological structure descriptor. We present
the extremal trees and unicyclic graphs with maximum and minimum eccentric
connectivity index subject to the certain graph constraints. Sharp lower and
asymptotic upper bound for all graphs are given and various connections with
other important graph invariants are established. In addition, we present
explicit formulae for the values of eccentric connectivity index for several
families of composite graphs and designed a linear algorithm for calculating
the eccentric connectivity index of trees. Some open problems and related
indices for further study are also listed.Comment: 25 pages, 5 figure
On the extremal properties of the average eccentricity
The eccentricity of a vertex is the maximum distance from it to another
vertex and the average eccentricity of a graph is the mean value
of eccentricities of all vertices of . The average eccentricity is deeply
connected with a topological descriptor called the eccentric connectivity
index, defined as a sum of products of vertex degrees and eccentricities. In
this paper we analyze extremal properties of the average eccentricity,
introducing two graph transformations that increase or decrease .
Furthermore, we resolve four conjectures, obtained by the system AutoGraphiX,
about the average eccentricity and other graph parameters (the clique number,
the Randi\' c index and the independence number), refute one AutoGraphiX
conjecture about the average eccentricity and the minimum vertex degree and
correct one AutoGraphiX conjecture about the domination number.Comment: 15 pages, 3 figure
Soft clustering analysis of galaxy morphologies: A worked example with SDSS
Context: The huge and still rapidly growing amount of galaxies in modern sky
surveys raises the need of an automated and objective classification method.
Unsupervised learning algorithms are of particular interest, since they
discover classes automatically. Aims: We briefly discuss the pitfalls of
oversimplified classification methods and outline an alternative approach
called "clustering analysis". Methods: We categorise different classification
methods according to their capabilities. Based on this categorisation, we
present a probabilistic classification algorithm that automatically detects the
optimal classes preferred by the data. We explore the reliability of this
algorithm in systematic tests. Using a small sample of bright galaxies from the
SDSS, we demonstrate the performance of this algorithm in practice. We are able
to disentangle the problems of classification and parametrisation of galaxy
morphologies in this case. Results: We give physical arguments that a
probabilistic classification scheme is necessary. The algorithm we present
produces reasonable morphological classes and object-to-class assignments
without any prior assumptions. Conclusions: There are sophisticated automated
classification algorithms that meet all necessary requirements, but a lot of
work is still needed on the interpretation of the results.Comment: 18 pages, 19 figures, 2 tables, submitted to A
A note on multiplicative sum Zagreb index
Abstract For a nontrivial (molecular) graph G, its multiplicative sum Zagreb index, denoted by π * 1 (G), is defined as the product of the sum is the degree of vertex u. In this note, we establish a relationship between π * 1 (G) of a graph and the first multiplicative Zagreb index of its total graph. Moreover, we present some bounds for π * 1 (G) in terms of some other graph parameters including the second multiplicative Zagreb index, radius, the first Zagreb index
- …