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Found 7 papers, 3 papers with code
A statistical framework for GWAS of high dimensional phenotypes using summary statistics, with application to metabolite GWAS
The recent explosion of genetic and high dimensional biobank and 'omic' data has provided researchers with the opportunity to investigate the shared genetic origin (pleiotropy) of hundreds to thousands of related phenotypes.
Spectral embedding and the latent geometry of multipartite networks
Spectral embedding finds vector representations of the nodes of a network, based on the eigenvectors of its adjacency or Laplacian matrix, and has found applications throughout the sciences.
Multiple Network Embedding for Anomaly Detection in Time Series of Graphs
We examine two related, complementary inference tasks: the detection of anomalous graphs within a time series, and the detection of temporally anomalous vertices.
Methodology
On spectral algorithms for community detection in stochastic blockmodel graphs with vertex covariates
In network inference applications, it is often desirable to detect community structure, namely to cluster vertices into groups, or blocks, according to some measure of similarity.
Spectral inference for large Stochastic Blockmodels with nodal covariates
In many applications of network analysis, it is important to distinguish between observed and unobserved factors affecting network structure.
On a 'Two Truths' Phenomenon in Spectral Graph Clustering
Clustering is concerned with coherently grouping observations without any explicit concept of true groupings.
A statistical interpretation of spectral embedding: the generalised random dot product graph
Spectral embedding is a procedure which can be used to obtain vector representations of the nodes of a graph.
