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Spectral embedding is a procedure which can be used to obtain vector representations of the nodes of a graph.
Spectral clustering and the high-dimensional stochastic blockmodel
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On two distinct sources of nonidentifiability in latent position random graph models
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Learning 1-dimensional submanifolds for subsequent inference on random dot product graphs
Trosset, M. W., Gao, M., Tang, M., and Priebe, C. E. (2020) · 2004
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CSNE: Conditional signed network embedding
Mara, A., Mashayekhi, Y., Lijffijt, J., and De Bie, T. (2020) · 2005
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Random Dot Product Graphs: A Model for Social Networks
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Automatic dimensionality selection from the scree plot via the use of profile likelihood
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A tutorial on spectral clustering
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Young, S. J. and Scheinerman, E. R. (2007) · 2007
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A limit theorem for scaled eigenvectors of random dot product graphs
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Network-wide anomaly detection via the Dirichlet process
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Cybersecurity data sources for dynamic network research
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Disassortivity of computer networks
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Community detection and stochastic block models: recent developments
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Spectral clustering on spherical coordinates under the degree-corrected stochastic blockmodel
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Large networks and graph limits. American Mathematical Society Colloquium Publications
Lovász, L. (2012) · 2012
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A consistent adjacency spectral embedding for stochastic blockmodel graphs
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Spectral statistics of Erdős-Rényi’ graphs I: Local semicircle law
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Spectra of edge-independent random graphs
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Statistical inference on random dot product graphs: a survey
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Community detection and classification in hierarchical stochastic blockmodels
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Estimating mixed memberships with sharp eigenvector deviations
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Network representation using graph root distributions
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Limit theorems for eigenvectors of the normalized laplacian for random graphs
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Manifold structure in graph embeddings
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On estimation and inference in latent structure random graphs
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