Multiway Spectral Partitioning and Higher-Order Cheeger Inequalities
By James R. Lee, Shayan Oveis Gharan, Luca Trevisan. 2014
167 citations recorded by the source corpus.
Subfields: Computational Theory and Mathematics, Discrete Mathematics and Combinatorics, Geometry and Topology
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In Computational Theory and Mathematics
In Geometry and Topology
Citations in the corpus
- CaSpeR: Latent Spectral Regularization for Continual Learning (2023, background): “Since the number of eigenvalues close to zero corresponds to the number of loosely connected partitions within the graph [28], our loss indirectly encourages the points in the buffer to be clustered without strict supervision.”
- Simple Spectral Graph Convolution (2021, background): “Higher-order Cheeger’s inequality (Bandeira et al., 2013; Lee et al., 2014) bridges the gap between the network spectral analysis and graph partitioning by controlling the bounds of k -way Cheeger constant: where λ k is the k-th eigenvalue of the normalized Laplacian matrix and 0 = λ 1 ≤ λ 2 ≤ · ·…”