The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Spectral redemption in clustering sparse networks
Open accessAbstract
Significance: Spectral algorithms are widely applied to data clustering problems, including finding communities or partitions in graphs and networks. We propose a way of encoding sparse data using a “nonbacktracking” matrix, and show that the corresponding spectral algorithm performs optimally for some popular generative models, including the stochastic block model. This is in contrast with classical spectral algorithms, based on the adjacency matrix, random walk matrix, and graph Laplacian, which perform poorly in the sparse case, failing significantly above a recently discovered phase transition for the detectability of communities. Further support for the method is provided by experiments on real networks as well as by theoretical arguments and analogies from probability theory, statistical physics, and the theory of random matrices.
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Cites
The political blogosphere and the 2004 U.S. election: divided they blog
Adamic, L. A., & Glance, N. (2005) · Proceedings of the 3rd international workshop on Link discovery · doi:10.1145/1134271.1134277 · Cited by 2,619
The Largest Eigenvalue of Sparse Random Graphs
KRIVELEVICH, M., & SUDAKOV, B. (2003) · Combinatorics, Probability and Computing · doi:10.1017/s0963548302005424 · Cited by 207 · cited 2×
“[18] Krivelevich M and Sudakov B (2003).”
Background
Cited by
Community detection in networks: a user guide
Fortunato, S., & Hric, D. (2016) · Physics Reports · doi:10.1016/j.physrep.2016.09.002 · Cited by 2,086
Community Detection and Stochastic Block Models
Abbe, E. (2018) · now publishers, Inc. eBooks · doi:10.1561/9781680834772 · Cited by 544
Percolation on complex networks: Theory and application
Li, M., Liu, R.-R., Lü, L., Hu, M.-B., Xu, S., & Zhang, Y.-C. (2021) · Physics Reports · doi:10.1016/j.physrep.2020.12.003 · Cited by 365
Order and stochasticity in the folding of individual Drosophila genomes
Ulianov, S. V., Zakharova, V. V., Galitsyna, A. A., Kos, P. I., Polovnikov, K. E., Flyamer, I. M., Mikhaleva, E. A., Khrameeva, E. E., Germini, D., Logacheva, M. D., et al. (2021) · Nature Communications · doi:10.1038/s41467-020-20292-z · Cited by 64
The Limits of Query Monitoring against Distributed Model Extraction
Khilar, S. C. (2026) · arXiv
“…heavy accounts is the localisation of edge eigenvectors on high-degree vertices (Alt et al., 2023; Hiesmayr & McKenzie, 2023), whose standard repairs are regularisation and non-backtracking operators (Le et al., 2017; Krzakala et al., 2013; Saade et al., 2014); hollowing belongs to this family.”
Metadata
- Authors
- Krzakala, F., Moore, C., Mossel, E., Neeman, J., Sly, A., Zdeborová, L., & Zhang, P.
- Venue
- Proceedings of the National Academy of Sciences
- Published
- 2013-11-25
- Volume
- 110
- Issue
- 52
- Pages
- 20935-20940
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- OpenAlex
- W2024514015
- Cited by
- 638
- References
- 2
- FWCI
- 33.72
- Type
- journal article
- Language
- en
- Open access
- green
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- Krzakala, F., Moore, C., Mossel, E., Neeman, J., Sly, A., Zdeborová, L., & Zhang, P. (2013). Spectral redemption in clustering sparse networks. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.1312486110
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