Minimal interval completion through graph exploration
نویسندگان
چکیده
منابع مشابه
Minimal Interval Completion Through Graph Exploration
Given an arbitrary graph G = (V,E) and an interval graph H = (V, F ) with E ⊆ F we say thatH is an interval completion of G. The graphH is called aminimal interval completion of G if, for any sandwich graph H ′ = (V, F ′) with E ⊆ F ′ ⊂ F , H ′ is not an interval graph. In this paper we give a O(nm) time algorithm computing a minimal interval completion of an arbitrary graph. The output is an i...
متن کاملAn O(n2) algorithm for the minimal interval completion problem
The minimal interval completion problem consists in adding edges to an arbitrary graph so that the resulting graph is an interval graph; the objective is to add an inclusion minimal set of edges, which means that no proper subset of the added edges can result in an interval graph when added to the original graph. We give an O(n) time algorithm to obtain a minimal interval completion of an
متن کاملOrdering Problems Approximated: Single-Processor Scheduling and Interval Graph Completion
In this paper, we give the first polynomial time approximation algorithms for two problems in combinatorial optimization. The first problem is single-processor scheduling to minimize weighted sum of completion times, subject to precedence constraints. The second problem, interval graph completion, is finding a minimum-size interval graph containing the input graph as a subgraph. Both problems a...
متن کاملGraph Matrix Completion in Presence of Outliers
Matrix completion problem has gathered a lot of attention in recent years. In the matrix completion problem, the goal is to recover a low-rank matrix from a subset of its entries. The graph matrix completion was introduced based on the fact that the relation between rows (or columns) of a matrix can be modeled as a graph structure. The graph matrix completion problem is formulated by adding the...
متن کاملGraph Convolutional Matrix Completion
We consider matrix completion for recommender systems from the point of view of link prediction on graphs. Interaction data such as movie ratings can be represented by a bipartite user-item graph with labeled edges denoting observed ratings. Building on recent progress in deep learning on graph-structured data, we propose a graph auto-encoder framework based on differentiable message passing on...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Theoretical Computer Science
سال: 2009
ISSN: 0304-3975
DOI: 10.1016/j.tcs.2008.09.053