A proof for a conjecture on the Randić index of graphs with diameter

AbstractThe Randić index R(G) of a graph G is defined by R(G)=∑uv1d(u)d(v), where d(u) is the degree of a vertex u in G and the summation extends over all edges uv of G. Aouchiche et al. proposed a conjecture on the relationship between the Randić index and the diameter: for any connected graph on n≥3 vertices with the Randić index R(G) and the diameter D(G), R(G)−D(G)≥2−n+12andR(G)D(G)≥n−3+222n−2, with equalities if and only if G is a path. In this work, we show that this conjecture is true for trees. Furthermore, we prove that for any connected graph on n≥3 vertices with the Randić index R(G) and the diameter D(G), R(G)−D(G)≥2−n+12, with equality if and only if G is a path

On the Randić index and girth of graphs

AbstractThe Randić index R(G) of a graph G is defined by R(G)=∑uv1d(u)d(v), where d(u) is the degree of a vertex u in G and the summation extends over all edges uv of G. In this work, we give a sharp upper bound and a lower bound of the Randić index among connected n-vertex graphs with girth g≥k(k≥3)

Distance-unbalancedness of graphs

In this paper we propose and study a new structural invariant for graphs, called distance-unbalanced\-ness, as a measure of how much a graph is (un)balanced in terms of distances. Explicit formulas are presented for several classes of well-known graphs. Distance-unbalancedness of trees is also studied. A few conjectures are stated and some open problems are proposed.Comment: 14 pages, 3 figure

Some properties on the lexicographic product of graphs obtained by monogenic semigroups

In (Das et al. in J. Inequal. Appl. 2013:44, 2013), a new graph Gamma (S-M) on monogenic semigroups S-M (with zero) having elements {0, x, x(2), x(3),..., x(n)} was recently defined. The vertices are the non-zero elements x, x(2), x(3),..., x(n) and, for 1 <= i, j <= n, any two distinct vertices x(i) and x(j) are adjacent if x(i)x(j) = 0 in S-M. As a continuing study, in an unpublished work, some well-known indices (first Zagreb index, second Zagreb index, Randic index, geometric-arithmetic index, atom-bond connectivity index, Wiener index, Harary index, first and second Zagreb eccentricity indices, eccentric connectivity index, the degree distance) over Gamma (S-M) were investigated by the same authors of this paper. In the light of the above references, our main aim in this paper is to extend these studies to the lexicographic product over Gamma (S-M). In detail, we investigate the diameter, radius, girth, maximum and minimum degree, chromatic number, clique number and domination number for the lexicographic product of any two (not necessarily different) graphs Gamma (S-M(1)) and Gamma (S-M(2)).Selçuk ÜniversitesiSungkyunkwan University (BK21

Guarantees for Efficient and Adaptive Online Learning

In this thesis, we study the problem of adaptive online learning in several different settings. We first study the problem of predicting graph labelings online which are assumed to change over time. We develop the machinery of cluster specialists which probabilistically exploit any cluster structure in the graph. We give a mistake-bounded algorithm that surprisingly requires only O(log n) time per trial for an n-vertex graph, an exponential improvement over existing methods. We then consider the model of non-stationary prediction with expert advice with long-term memory guarantees in the sense of Bousquet and Warmuth, in which we learn a small pool of experts. We consider relative entropy projection-based algorithms, giving a linear-time algorithm that improves on the best known regret bound. We show that such projection updates may be advantageous over previous "weight-sharing" approaches when weight updates come with implicit costs such as in portfolio optimization. We give an algorithm to compute the relative entropy projection onto the simplex with non-uniform (lower) box constraints in linear time, which may be of independent interest. We finally extend the model of long-term memory by introducing a new model of adaptive long-term memory. Here the small pool is assumed to change over time, with the trial sequence being partitioned into epochs and a small pool associated with each epoch. We give an efficient linear-time regret-bounded algorithm for this setting and present results in the setting of contextual bandits