A Study on Integer Additive Set-Valuations of Signed Graphs

Let $\N$ denote the set of all non-negative integers and \cP(\N) be its power set. An integer additive set-labeling (IASL) of a graph $G$ is an injective set-valued function f:V(G)\to \cP(\N)-\{\emptyset\} such that the induced function f^+:E(G) \to \cP(\N)-\{\emptyset\} is defined by $f^+ (uv) = f(u)+ f(v)$, where $f(u)+f(v)$ is the sumset of $f(u)$ and $f(v)$. A graph which admits an IASL is usually called an IASL-graph. An IASL $f$ of a graph $G$ is said to be an integer additive set-indexer (IASI) of $G$ if the associated function $f^+$ is also injective. In this paper, we define the notion of integer additive set-labeling of signed graphs and discuss certain properties of signed graphs which admits certain types of integer additive set-labelings.Comment: 12 pages, Carpathian Mathematical Publications, Vol. 8, Issue 2, 2015, 12 page

A Characterisation of Weak Integer Additive Set-Indexers of Graphs

An integer additive set-indexer is defined as an injective function $f:V(G)\rightarrow 2^{\mathbb{N}_0}$ such that the induced function $g_f:E(G) \rightarrow 2^{\mathbb{N}_0}$ defined by $g_f (uv) = f(u)+ f(v)$ is also injective. An integer additive set-indexer is said to be $k$-uniform if $|g_f(e)| = k$ for all $e\in E(G)$. An integer additive set-indexer $f$ is said to be a weak integer additive set-indexer if $|g_f(uv)|=max(|f(u)|,|f(v)|)$ for all $u,v\in V(G)$. In this paper, we study the characteristics of certain graphs and graph classes which admit weak integer additive set-indexers.Comment: 12pages, 4 figures, arXiv admin note: text overlap with arXiv:1311.085

Linear tolls suffice: New bounds and algorithms for tolls in single source networks

AbstractWe show that tolls that are linear in the latency of the maximum latency path are necessary and sufficient to induce heterogeneous network users to independently choose routes that lead to traffic with minimum average latency. This improves upon the earlier bound of O(n3lmax) given by Cole, Dodis, and Roughgarden in STOC 03. (Here, n is the number of nodes in the network; and lmax is the maximum latency of any edge.) Our proof is also simpler, relating the Nash flow to the optimal flow as flows rather than cuts.We model the set of users as the set [0,1] ordered by their increasing willingness to pay tolls to reduce latency—their valuation of time. Cole et al. give an algorithm that computes optimal tolls for a bounded number of agent valuations, under the very strong assumption that they know which path each user type takes in the Nash flow imposed by these (unknown) tolls. We show that in series parallel graphs, the set of paths traveled by users in any Nash flow with optimal tolls is independent of the distribution of valuations of time of the users. In particular, for any continuum of users (not restricted to a finite number of valuation classes) in series parallel graphs, we show how to compute these paths without knowing α.We give a simple example to demonstrate that if the graph is not series parallel, then the set of paths traveled by users in the Nash flow depends critically on the distribution of users’ valuations of time