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Graph Embeddings and Laplacian Eigenvalues
2000
SIAM Journal on Matrix Analysis and Applications
Graph embeddings are useful in bounding the smallest nontrivial eigenvalues of Laplacian matrices from below. For an n × n Laplacian, these embedding methods can be characterized as follows: The lower bound is based on a clique embedding into the underlying graph of the Laplacian. An embedding can be represented by a matrix Γ; the best possible bound based on this embedding is n/λmax(Γ T Γ), where λmax indicates the largest eigenvalue of the specified matrix. However, the best bounds produced
doi:10.1137/s0895479897329825
fatcat:slod5nz4e5hpzbrntz5iuci7gy