The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
Spectral partitioning of random graphs
Abstract
Problems such as bisection, graph coloring, and clique are generally believed hard in the worst case. However, they can be solved if the input data is drawn randomly from a distribution over graphs containing acceptable solutions. In this paper we show that a simple spectral algorithm can solve all three problems above in the average case, as well as a more general problem of partitioning graphs based on edge density. In nearly all cases our approach meets or exceeds previous parameters, while introducing substantial generality. We apply spectral techniques, using foremost the observation that in all of these problems, the expected adjacency matrix is a low rank matrix wherein the structure of the solution is evident.
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Cites
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Algorithms for graph partitioning on the planted partition model
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On clusterings-good, bad and spectral
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Cited by
Hierarchical structure and the prediction of missing links in networks
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Community Detection and Stochastic Block Models
Abbe, E. (2018) · now publishers, Inc. eBooks · doi:10.1561/9781680834772 · Cited by 544
Node Classification in Social Networks
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Metadata
- Authors
- McSherry, F.
- Venue
- Proceedings 42nd IEEE Symposium on Foundations of Computer Science
- Publisher
- IEEE
- Published
- 2001
- Pages
- 529-537
- ISSN
- 0272-5428 (ISSN-L)
- OpenAlex
- W1605711022
- Cited by
- 584
- References
- 15
- FWCI
- 10.65
- Type
- conference paper
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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