Efficient Reassembling of Three-Regular Planar Graphs

10 Jul 2018  ·  Assaf Kfoury, Benjamin Sisson ·

A reassembling of a simple graph G = (V,E) is an abstraction of a problem arising in earlier studies of network analysis. There are several equivalent definitions of graph reassembling; in this report we use a definition which makes it closest to the notion of graph carving. A reassembling is a rooted binary tree whose nodes are subsets of V and whose leaf nodes are singleton sets, with each of the latter containing a distinct vertex of G. The parent of two nodes in the reassembling is the union of the two children's vertex sets. The root node of the reassembling is the full set V. The edge-boundary degree of a node in the reassembling is the number of edges in G that connect vertices in the node's set to vertices not in the node's set. A reassembling's alpha-measure is the largest edge-boundary degree of any node in the reassembling. A reassembling of G is alpha-optimal if its alpha-measure is the minimum among all alpha-measures of G's reassemblings. The problem of finding an alpha-optimal reassembling of a simple graph in general was already shown to be NP-hard. In this report we present an algorithm which, given a 3-regular plane graph G = (V,E) as input, returns a reassembling of G with an alpha-measure independent of n (number of vertices in G) and upper-bounded by 2k, where k is the edge-outerplanarity of G. (Edge-outerplanarity is distinct but closely related to the usual notion of outerplanarity; as with outerplanarity, for a fixed edge-outerplanarity k, the number n of vertices can be arbitrarily large.) Our algorithm runs in time linear in n. Moreover, we construct a class of $3$-regular plane graphs for which this alpha-measure is optimal, by proving that 2k is the lower bound on the alpha-measure of any reassembling of a graph in that class.

PDF Abstract
No code implementations yet. Submit your code now

Categories


Data Structures and Algorithms 68Q99, 68W40, 05C10 C.m; F.2; F.m

Datasets


  Add Datasets introduced or used in this paper