No abstract

We define the following parameter of connected graphs. For a given graph G = (V, E) we place one agent in each vertex v ∈ V . Every pair of agents sharing a common edge is declared to be acquainted. In each round we choose some matching of G (not necessarily a maximal matching), and for each edge in the matching the agents on this edge swap places. After the swap, again, every pair of agents sharing a common edge become acquainted, and the process continues. We define the acquaintance time of a graph G, denoted by AC(G), to be the minimal number of rounds required until every two agents are acquainted. We first study the acquaintance time for some natural families of graphs including the path, expanders, the binary tree, and the complete bipartite graph. We also show that for all n ∈ N and for all positive integers k ≤ n 1.5 there exists an n-vertex graph G such that k/c ≤ AC(G) ≤ c · k for some universal constant c ≥ 1. We also prove that for all n-vertex connected graphs G we have AC(G) = O( n 2 log(n)/ log log(n) ), thus improving the trivial upper bound of O(n 2 ) achieved by sequentially letting each agent perform depth-first search along some spanning tree of G. Studying the computational complexity of this problem, we prove that for any constant t ≥ 1 the problem of deciding that a given graph G has AC(G) ≤ t or AC(G) ≥ 2t is N P-complete. That is, AC(G) is N P-hard to approximate within multiplicative factor of 2, as well as within any additive constant factor. On the algorithmic side, we give a deterministic algorithm that given an n-vertex graph G with AC(G) = 1 finds a strategy for acquaintance that consists of n/c matchings in time n c+O(1) . We also design a randomized polynomial time algorithm that given an n-vertex graph G with AC(G) = 1 finds with high probability an O(log(n))-rounds strategy for acquaintance.

No abstract

No abstract

We initiate the study of testing properties of images that correspond to sparse 0/1-valued matrices of size n×n. Our study is related to but different from the study initiated by Raskhodnikova (Proceedings of RANDOM, 2003 ), where the images correspond to dense 0/1-valued matrices. Specifically, while distance between images in the model studied by Raskhodnikova is the fraction of entries on which the images differ taken with respect to all n 2 entries, the distance measure in our model is defined by the fraction of such entries taken with respect to the actual number of 1's in the matrix. We study several natural properties: connectivity, convexity, monotonicity, and being a line. In all cases we give testing algorithms with sublinear complexity, and in some of the cases we also provide corresponding lower bounds.