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Found 16 papers, 9 papers with code
T-Stochastic Graphs
These alternative models motivate a novel approach to hierarchical clustering that combines spectral techniques with the well-known Neighbor-Joining algorithm from phylogenetic reconstruction.
Estimating Graph Dimension with Cross-validated Eigenvalues
In this situation, one could argue that the correct choice of $k$ is the number of detectable dimensions.
A New Basis for Sparse Principal Component Analysis
Previous versions of sparse principal component analysis (PCA) have presumed that the eigen-basis (a $p \times k$ matrix) is approximately sparse.
Targeted sampling from massive block model graphs with personalized PageRank
Using the degree-corrected stochastic block model, we study whether the PPR vector can select nodes that belong to the same block as the seed node.
Understanding Regularized Spectral Clustering via Graph Conductance
The second part of the paper starts from a previously proposed form of regularized spectral clustering and shows that it is related to the graph conductance on a `regularized graph'.
Discovering Political Topics in Facebook Discussion threads with Graph Contextualization
In the Facebook data, spectral clustering without the contextualizing text information finds a mixture of (i) candidate and (ii) issue clusters.
Generalized least squares can overcome the critical threshold in respondent-driven sampling
In particular, a theoretical analysis indicates that the variance of the GLS estimator is $O(n^{-1})$.
Statistics Theory Social and Information Networks Probability Statistics Theory
Novel Sampling Design for Respondent-driven Sampling
As such, even under the ideal sampling assumptions, the performance of RDS is restricted by the underlying social network: if the network is divided into communities that are weakly connected to each other, then RDS is likely to oversample one of these communities.
Methodology
Asymptotic Theory for Estimating the Singular Vectors and Values of a Partially-observed Low Rank Matrix with Noise
In practice, the singular vectors and singular values of the low rank matrix play a pivotal role for statistical analyses and inferences.
Methodology
Network driven sampling; a critical threshold for design effects
Under certain assumptions on the referral tree, the design effect of network sampling has a critical threshold that is a function of the referral rate $m$ and the clustering structure in the social network, represented by the second eigenvalue of the Markov transition matrix, $\lambda_2$.
A note relating ridge regression and OLS p-values to preconditioned sparse penalized regression
When the design matrix has orthonormal columns, "soft thresholding" the ordinary least squares (OLS) solution produces the Lasso solution [Tibshirani, 1996].
Covariate-assisted spectral clustering
We utilize these node covariates to help uncover latent communities in a graph, using a modification of spectral clustering.
Fantope Projection and Selection: A near-optimal convex relaxation of sparse PCA
We propose a novel convex relaxation of sparse principal subspace estimation based on the convex hull of rank-$d$ projection matrices (the Fantope).
Regularized Spectral Clustering under the Degree-Corrected Stochastic Blockmodel
Spectral clustering is a fast and popular algorithm for finding clusters in networks.
The blessing of transitivity in sparse and stochastic networks
The only constraint on the ambient graph is that it is large and sparse--it could be generated at random or by an adversary--suggesting a theoretical explanation for the robust empirical performance of local clustering algorithms.
Co-clustering for directed graphs: the Stochastic co-Blockmodel and spectral algorithm Di-Sim
In each example, a small subset of nodes have persistent asymmetries; these nodes send edges with one cluster, but receive edges with another cluster.
