Switching Checkerboards
David Ellison1 Bertrand Jouve2 Lewi Stone1
1RMIT University, School of Science 2Universit´e de Toulouse, CNRS
1 Introduction
Zagreb Indices
Description of the Problem
2 Switching a Checkerboard
Checkerboards Graph of Matrices Positive Checkerboards
3 Successive Positive Switches
Simulations and Observations Overall variation of λ1
Zagreb Indices
Let G = (V , E ) be a simple graph with n vertices and m edges.
M1= P i ∈V di2 M2= P {ij}∈E didj, Z1 = q M1 n ≤ λ1 Z2 = q M2 m
Z1 is the quadratic average of the degrees and a lower bound
of the spectral radius λ1.
Z2 is an average over the edges ofpdidj and gives a loose
Description of the problem
We consider simple undirected graphs. We fix a degree distribution.
Checkerboard
A checkerboard in a graph is an alternating cycle of 2 edges and 2 non-edges. The operation of switching a checkerboard consists of switching the edges and non-edges.
In the adjacency matrix, the switch affects a 2 × 2 sub-matrix and its symmetric: 1 0 0 1 ←→ 0 1 1 0
Graph of matrices
Theorem (Ryser, 1963)
Any two graphs with identical degree distribution can be linked through sequence of switches.
Orienting the Graph of matrices
By distinguishing positive checkerboards1 0 0 1 and negative checkerboards0 1 1 0 , and only allowing switches from negative to positive, we define an orientation for the graph of matrices.Proposition
The directed graph of matrices is acyclic.
Idea of proof: The quantity I (A) =P ij aij increases along the
Effect of a switch
Let A0 be obtained from A by switching a checkerboard of
coordinates (i , j , k, l ). Let X and X0 be the normalised principal eigenvectors of A and A0, respectively.
We have: λ01− λ1 = tX0A0X0− tXAX ≥ tXA0X −tXAX = tX (A0−A)X . Thus λ01− λ1≥ 2(xi− xj)(xk− xl). 0 1 1 0 1 0 0 1 1 0 0 1 0 1 1 0 xi xk xj xl xi xk xj xl Similarly, M20 − M2 = (di− dj)(dk− dl). Remark
If the rows and columns of A are ordered increasingly (or
decreasingly), M2 increases with each positive switch and λ1
Sources and Sinks
Theorem
The sources and sinks of the directed graph of matrices are,
respectively, the local minima and maxima for M2 and Z2.
Theorem
The global maximum of λ1 is reached at a sink of the directed
graph of matrices.
Key to the proof: If λ1 is maximised, the coefficients of the
Simulations
Starting from a random graph with 20 vertices and 25 edges, successive random positive switches are applied.
Simulations
Starting from an Erd˝os-R´enyi graph with 50 vertices (p = .2),
Observations
Z2 is non-decreasing, but λ2 may locally decrease slightly. (As
expected)
Generally, for large graphs, the amplitude of the total variation is fairly small. However, some theoretical examples yield arbitrarily large variations.
For regular graphs, λ1 and Z2 do not vary at all.
Large variations of λ
1 λ1 = 2−→
λ1= 4 Degree distribution {4,4,4,4,4,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1} nK1,n−1 −→ Kn+ n(n−1)2 K2 λ1= Z2 = √ n − 1 << λ1= n − 1, Z2≈ n−1√2Concavity
The observed concavity of λ1 may be explained by the following
result:
Theorem
For an Erd˝os-R´enyi graph, the average variation of λ1 caused by a
positive switch decreases on average after a positive switch.
Elements of proof: The average variation of λ caused by a switch is twice the average checkerboard area (xi − xj)(xk − xl). After a
positive switch, some remaining checkerboards have their area reduced, but some small checkerboards disappear. The key is to verify that the former has a greater effect than the latter on the average checkerboard area.
Note that some carefully crafted pathological examples yield a convex increase of λ1.
Discrepancy between Z
2and λ
1For kite graphs, λ1 can be arbitrarily greater than Z2. (Stevanovi´c & Hansen, 2008)
For double stars, Z2
λ1 can be arbitrarily close to
q
3
2. (Abdo et al., 2014)
For certain cographs also, Z2
λ1 can be arbitrarily close to
q
3 2.
Conjecture: Revisiting a problem by Nikiforov (2006)
For any graph, Z2
λ1 <
q
3
2. Furthermore, for a fixed n, the
maximum of Z2