Efficient Minimum Weight Vertex Cover Heuristics Using Graph Neural Networks Article Swipe
Quentin Cappart
,
Didier Chételat
,
Elias L. Khalil
,
Andrea Lodi
,
Christopher G. Morris
,
Petar Veličković
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.4230/lipics.sea.2022.12
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.4230/lipics.sea.2022.12
Minimum weighted vertex cover is the NP-hard graph problem of choosing a subset of vertices incident to all edges such that the sum of the weights of the chosen vertices is minimum. Previous efforts for solving this in practice have typically been based on search-based iterative heuristics or exact algorithms that rely on reduction rules and branching techniques. Although exact methods have shown success in solving instances with up to millions of vertices efficiently, they are limited in practice due to the NP-hardness of the problem. We present a new hybrid method that combines elements from exact methods, iterative search, and graph neural networks (GNNs). More specifically, we first compute a greedy solution using reduction rules whenever possible. If no such rule applies, we consult a GNN model that selects a vertex that is likely to be in or out of the solution, potentially opening up for further reductions. Finally, we use an improved local search strategy to enhance the solution further. Extensive experiments on graphs of up to a billion edges show that the proposed GNN-based approach finds better solutions than existing heuristics. Compared to exact solvers, the method produced solutions that are, on average, 0.04% away from the optimum while taking less time than all state-of-the-art alternatives.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SEA.2022.12
- OA Status
- green
- Cited By
- 19
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4287326275
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4287326275Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.4230/lipics.sea.2022.12Digital Object Identifier
- Title
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Efficient Minimum Weight Vertex Cover Heuristics Using Graph Neural NetworksWork title
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-01-01Full publication date if available
- Authors
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Quentin Cappart, Didier Chételat, Elias L. Khalil, Andrea Lodi, Christopher G. Morris, Petar VeličkovićList of authors in order
- Landing page
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https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SEA.2022.12Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
- OA URL
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https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SEA.2022.12Direct OA link when available
- Concepts
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Computer science, Combinatorial optimization, Key (lock), Artificial intelligence, Theoretical computer science, Artificial neural network, Graph, Quadratic assignment problem, Machine learning, Algorithm, Computer securityTop concepts (fields/topics) attached by OpenAlex
- Cited by
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19Total citation count in OpenAlex
- Citations by year (recent)
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2025: 2, 2024: 11, 2023: 4, 2022: 2Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.minimum. | 31 |
| abstract_inverted_index.networks | 103 |
| abstract_inverted_index.practice | 38, 78 |
| abstract_inverted_index.problem. | 85 |
| abstract_inverted_index.produced | 190 |
| abstract_inverted_index.proposed | 175 |
| abstract_inverted_index.solution | 112, 160 |
| abstract_inverted_index.solvers, | 187 |
| abstract_inverted_index.strategy | 156 |
| abstract_inverted_index.vertices | 14, 29, 72 |
| abstract_inverted_index.weighted | 1 |
| abstract_inverted_index.whenever | 116 |
| abstract_inverted_index.Extensive | 162 |
| abstract_inverted_index.GNN-based | 176 |
| abstract_inverted_index.branching | 56 |
| abstract_inverted_index.instances | 66 |
| abstract_inverted_index.iterative | 45, 98 |
| abstract_inverted_index.possible. | 117 |
| abstract_inverted_index.reduction | 53, 114 |
| abstract_inverted_index.solution, | 142 |
| abstract_inverted_index.solutions | 180, 191 |
| abstract_inverted_index.typically | 40 |
| abstract_inverted_index.algorithms | 49 |
| abstract_inverted_index.heuristics | 46 |
| abstract_inverted_index.NP-hardness | 82 |
| abstract_inverted_index.experiments | 163 |
| abstract_inverted_index.heuristics. | 183 |
| abstract_inverted_index.potentially | 143 |
| abstract_inverted_index.reductions. | 148 |
| abstract_inverted_index.techniques. | 57 |
| abstract_inverted_index.efficiently, | 73 |
| abstract_inverted_index.search-based | 44 |
| abstract_inverted_index.alternatives. | 208 |
| abstract_inverted_index.specifically, | 106 |
| abstract_inverted_index.state-of-the-art | 207 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 94 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile.value | 0.92439682 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |