Adding Edges for Maximizing Weighted Reachability

In this paper, we consider the problem of improving the reachability of a graph. We approach the problem from a graph augmentation perspective, in which a limited set size of edges is added to the graph to increase the overall number of reachable nodes. We call this new problem the <i>Maximum...

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出版年:Algorithms
主要な著者: Federico Corò, Gianlorenzo D'Angelo, Cristina M. Pinotti
フォーマット: 論文
言語:英語
出版事項: MDPI AG 2020-03-01
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オンライン・アクセス:https://www.mdpi.com/1999-4893/13/3/68
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author Federico Corò
Gianlorenzo D'Angelo
Cristina M. Pinotti
author_facet Federico Corò
Gianlorenzo D'Angelo
Cristina M. Pinotti
author_sort Federico Corò
collection DOAJ
container_title Algorithms
description In this paper, we consider the problem of improving the reachability of a graph. We approach the problem from a graph augmentation perspective, in which a limited set size of edges is added to the graph to increase the overall number of reachable nodes. We call this new problem the <i>Maximum Connectivity Improvement</i> (MCI) problem. We first show that, for the purpose of solve solving MCI, we can focus on Directed Acyclic Graphs (DAG) only. We show that approximating the MCI problem on DAG to within any constant factor greater than <inline-formula> <math display="inline"> <semantics> <mrow> <mn>1</mn> <mo>&#8722;</mo> <mn>1</mn> <mspace width="-1.111pt"></mspace> <mo>/</mo> <mspace width="-0.55542pt"></mspace> <mi>e</mi> </mrow> </semantics> </math> </inline-formula> is <inline-formula> <math display="inline"> <semantics> <mi mathvariant="italic">NP</mi> </semantics> </math> </inline-formula>-hard even if we restrict to graphs with a single source or a single sink, and the problem remains <inline-formula> <math display="inline"> <semantics> <mi mathvariant="italic">NP</mi> </semantics> </math> </inline-formula>-complete if we further restrict to unitary weights. Finally, this paper presents a dynamic programming algorithm for the MCI problem on trees with a single source that produces optimal solutions in polynomial time. Then, we propose two polynomial-time greedy algorithms that guarantee <inline-formula> <math display="inline"> <semantics> <mrow> <mo>(</mo> <mn>1</mn> <mo>&#8722;</mo> <mn>1</mn> <mspace width="-1.111pt"></mspace> <mo>/</mo> <mspace width="-0.55542pt"></mspace> <mi>e</mi> <mo>)</mo> </mrow> </semantics> </math> </inline-formula>-approximation ratio on DAGs with a single source, a single sink or two sources.
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spelling doaj-art-e11a4efd97a342eaa1da0ee3ce3f3fa62025-08-19T19:20:30ZengMDPI AGAlgorithms1999-48932020-03-011336810.3390/a13030068a13030068Adding Edges for Maximizing Weighted ReachabilityFederico Corò0Gianlorenzo D'Angelo1Cristina M. Pinotti2Department of Computer Science, Sapienza University of Rome, 00161 Rome, ItalyGran Sasso Science Institute (GSSI), 67100 L’Aquila, ItalyDepartment of Computer Science and Mathematics, University of Perugia, 06123 Perugia PG, ItalyIn this paper, we consider the problem of improving the reachability of a graph. We approach the problem from a graph augmentation perspective, in which a limited set size of edges is added to the graph to increase the overall number of reachable nodes. We call this new problem the <i>Maximum Connectivity Improvement</i> (MCI) problem. We first show that, for the purpose of solve solving MCI, we can focus on Directed Acyclic Graphs (DAG) only. We show that approximating the MCI problem on DAG to within any constant factor greater than <inline-formula> <math display="inline"> <semantics> <mrow> <mn>1</mn> <mo>&#8722;</mo> <mn>1</mn> <mspace width="-1.111pt"></mspace> <mo>/</mo> <mspace width="-0.55542pt"></mspace> <mi>e</mi> </mrow> </semantics> </math> </inline-formula> is <inline-formula> <math display="inline"> <semantics> <mi mathvariant="italic">NP</mi> </semantics> </math> </inline-formula>-hard even if we restrict to graphs with a single source or a single sink, and the problem remains <inline-formula> <math display="inline"> <semantics> <mi mathvariant="italic">NP</mi> </semantics> </math> </inline-formula>-complete if we further restrict to unitary weights. Finally, this paper presents a dynamic programming algorithm for the MCI problem on trees with a single source that produces optimal solutions in polynomial time. Then, we propose two polynomial-time greedy algorithms that guarantee <inline-formula> <math display="inline"> <semantics> <mrow> <mo>(</mo> <mn>1</mn> <mo>&#8722;</mo> <mn>1</mn> <mspace width="-1.111pt"></mspace> <mo>/</mo> <mspace width="-0.55542pt"></mspace> <mi>e</mi> <mo>)</mo> </mrow> </semantics> </math> </inline-formula>-approximation ratio on DAGs with a single source, a single sink or two sources.https://www.mdpi.com/1999-4893/13/3/68graph augmentationapproximation algorithmsgreedy algorithmssubmodularitydagtreesdynamic programming
spellingShingle Federico Corò
Gianlorenzo D'Angelo
Cristina M. Pinotti
Adding Edges for Maximizing Weighted Reachability
graph augmentation
approximation algorithms
greedy algorithms
submodularity
dag
trees
dynamic programming
title Adding Edges for Maximizing Weighted Reachability
title_full Adding Edges for Maximizing Weighted Reachability
title_fullStr Adding Edges for Maximizing Weighted Reachability
title_full_unstemmed Adding Edges for Maximizing Weighted Reachability
title_short Adding Edges for Maximizing Weighted Reachability
title_sort adding edges for maximizing weighted reachability
topic graph augmentation
approximation algorithms
greedy algorithms
submodularity
dag
trees
dynamic programming
url https://www.mdpi.com/1999-4893/13/3/68
work_keys_str_mv AT federicocoro addingedgesformaximizingweightedreachability
AT gianlorenzodangelo addingedgesformaximizingweightedreachability
AT cristinampinotti addingedgesformaximizingweightedreachability