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The second eigenvalue of the Laplacian matrix and its associated eigenvector are fundamental features of an undirected graph, and as such they have found widespread use in scientific computing, machine learning, and data analysis.
A fast parametric maximum flow algorithm and applications
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An algorithm for drawing general undirected graphs
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Conductance and convergence of Markov chains—a combinatorial treatment of expanders
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Bi-CGSTAB: a fast and smoothly converging variant of Bi-CG for the solution of nonsymmetric linear systems
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Matrix Computations
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Spectral graph theory
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The anatomy of a large-scale hypertextual web search engine
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Normalized cuts and image segmentation
J. Shi and J. Malik · 2000
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Pajek—analysis and visualization of large networks
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Grouping with bias
S. X. Yu and J. Shi · 2002
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Topic-sensitive PageRank: A context-sensitive ranking algorithm for web search
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Scaling personalized web search
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Deeper inside PageRank
A. N. Langville and C. D. Meyer · 2004
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A survey on PageRank computing
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Normalized cuts revisited: A reformulation for segmentation with linear grouping constraints
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An algorithm for improving graph partitions
R. Andersen and K. Lang · 2008
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Statistical properties of community structure in large social and information networks
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Using contours to detect and localize junctions in natural images
M. Maire, P. Arbelaez, C. Fowlkes, and J. Malik · 2008
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On partitioning graphs via single commodity flows
L. Orecchia, L. Schulman, U.V. Vazirani, and N.K. Vishnoi · 2008
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Graph partitioning using single commodity flows
R. Khandekar, S. Rao, and U. Vazirani · 2006
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Finding community structure in networks using the eigenvectors of matrices
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Modularity and community structure in networks
M.E.J. Newman · 2006
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Empirical comparison of algorithms for network community detection
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Biased normalized cuts
S. Maji, N. K. Vishnoi, and J. Malik · 2011
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