Javascript must be enabled to continue!
Realizing the Asymmetric Index of a Graph
View through CrossRef
A graph G is asymmetric if its automorphism group is trivial. Asymmetric graphs were introduced by Erd\H{o}s and R\'{e}nyi Erdos [1]. They suggested the problem of starting with an asymmetric graph and removing some number, r , of edges and/or adding some number, s , of edges so that the resulting graph is non-asymmetric. Erd\H{o}s and R\'{e}nyi defined the degree of asymmetry of a graph to be the minimum value of r + s . In this paper, we consider another property that measures how close a given non-asymmetric graph is to being asymmetric. Brewer et al. defined the asymmetric index of a graph G , denoted a i ( G ) , as the minimum of r + s so that the resulting graph G is asymmetric [2]. It is noted that a i ( G ) is only defined for graphs with at least six vertices. We investigate the asymmetric index of both connected and disconnected graphs including paths, cycles, and grids, with the addition of up to two isolated vertices. Furthermore, for a graph in these families G , we determine the number of labeled asymmetric graphs that can be obtained by adding or removing a i ( G ) edges. This leads to the related question: Given a graph G where a i ( G ) = 1 , what is the probability that for a randomly chosen edge e , that G – e will be asymmetric? A graph is called minimally non-asymmetric if this probability is 1 . We give a construction of infinite families of minimally non-asymmetric graphs.
Title: Realizing the Asymmetric Index of a Graph
Description:
A graph G is asymmetric if its automorphism group is trivial.
Asymmetric graphs were introduced by Erd\H{o}s and R\'{e}nyi Erdos [1].
They suggested the problem of starting with an asymmetric graph and removing some number, r , of edges and/or adding some number, s , of edges so that the resulting graph is non-asymmetric.
Erd\H{o}s and R\'{e}nyi defined the degree of asymmetry of a graph to be the minimum value of r + s .
In this paper, we consider another property that measures how close a given non-asymmetric graph is to being asymmetric.
Brewer et al.
defined the asymmetric index of a graph G , denoted a i ( G ) , as the minimum of r + s so that the resulting graph G is asymmetric [2].
It is noted that a i ( G ) is only defined for graphs with at least six vertices.
We investigate the asymmetric index of both connected and disconnected graphs including paths, cycles, and grids, with the addition of up to two isolated vertices.
Furthermore, for a graph in these families G , we determine the number of labeled asymmetric graphs that can be obtained by adding or removing a i ( G ) edges.
This leads to the related question: Given a graph G where a i ( G ) = 1 , what is the probability that for a randomly chosen edge e , that G – e will be asymmetric? A graph is called minimally non-asymmetric if this probability is 1 .
We give a construction of infinite families of minimally non-asymmetric graphs.
Related Results
Graph convolutional neural networks for 3D data analysis
Graph convolutional neural networks for 3D data analysis
(English) Deep Learning allows the extraction of complex features directly from raw input data, eliminating the need for hand-crafted features from the classical Machine Learning p...
Bilangan Terhubung Titik Pelangi pada Graf Garis dan Graf Tengah dari Hasil Operasi Comb Graf Bintang C<sub>3</sub> dan Graf Bintang S<sub>n</sub>
Bilangan Terhubung Titik Pelangi pada Graf Garis dan Graf Tengah dari Hasil Operasi Comb Graf Bintang C<sub>3</sub> dan Graf Bintang S<sub>n</sub>
Penelitian ini bertujuan menentukan bilangan terhubung titik pelangi (rainbow vertex connection number) pada graf garis dan graf tengah yang diperoleh dari hasil operasi comb antar...
Bootstrapping a Biodiversity Knowledge Graph
Bootstrapping a Biodiversity Knowledge Graph
The "biodiversity knowledge graph" is a nice metaphor for connecting biodiversity data sources, but can we actually build it? Do we have sufficient linked data available? Given tha...
Abstract 902: Explainable AI: Graph machine learning for response prediction and biomarker discovery
Abstract 902: Explainable AI: Graph machine learning for response prediction and biomarker discovery
Abstract
Accurately predicting drug sensitivity and understanding what is driving it are major challenges in drug discovery. Graphs are a natural framework for captu...
Domination of Polynomial with Application
Domination of Polynomial with Application
In this paper, .We .initiate the study of domination. polynomial , consider G=(V,E) be a simple, finite, and directed graph without. isolated. vertex .We present a study of the Ira...
E-Cordial Labeling of Some Families of Graphs
E-Cordial Labeling of Some Families of Graphs
An E-cordial labeling σ: E →{0,1} induces σ∗: V →{0,1} on graph G=(V,E), where (σ(v)=(∑_(u∈V)▒〖σ(uv)〗) mod 2 is taken over all edges uv∈E, and the labelling satisfies the condition...
The quadrangle graph operator
The quadrangle graph operator
The cycle graph of a graph G is the graph [Formula: see text] whose vertices are the induced cycles of G and where two vertices are adjacent if and only if they are distinct induce...
A Scalable Data Structure for Efficient Graph Analytics and In-Place Mutations
A Scalable Data Structure for Efficient Graph Analytics and In-Place Mutations
The graph model enables a broad range of analysis, thus graph processing is an invaluable tool in data analytics. At the heart of every graph processing system lies a concurrent gr...

