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Low-rank network decomposition reveals structural characteristics of small-world networks

https://doi.org/10.1103/physreve.92.062822

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Community structure in social and biological networks

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Fast algorithm for detecting community structure in networks

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Finding community structure in very large networks

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Finding community structure in networks using the eigenvectors of matrices

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Approximating the largest eigenvalue of network adjacency matrices

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Efficient and principled method for detecting communities in networks

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Community Structure in Directed Networks

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Self-Sustained Activity in a Small-World Network of Excitable Neurons

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Synchronization Reveals Topological Scales in Complex Networks

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Sparse Representation for Computer Vision and Pattern Recognition

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RASL: Robust Alignment by Sparse and Low-Rank Decomposition for Linearly Correlated Images

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Cortical High-Density Counterstream Architectures

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A Singular Value Thresholding Algorithm for Matrix Completion

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Network Properties Revealed through Matrix Functions

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Rank-Sparsity Incoherence for Matrix Decomposition

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Augmented Lagrangian Method, Dual Methods, and Split Bregman Iteration for ROF, Vectorial TV, and High Order Models

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The Structure and Function of Complex Networks

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COMMUNITY STRUCTURE IN JAZZ

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Robust principal component analysis?

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Small-World Brain Networks

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Epidemics with two levels of mixing

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Critical paths in a metapopulation model of H1N1: Efficiently delaying influenza spreading through flight cancellation

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The Carbon Assimilation Network in Escherichia coli Is Densely Connected and Largely Sign-Determined by Directions of Metabolic Fluxes

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Matrix Computations

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Small worlds: the dynamics of networks between order and randomness

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