71 citations · 162 across the 14 of their papers we have counts for
17 papers
On inference for modularity statistics in structured networks
Anirban Mitra, Konasale Prasad, Joshua Cape
This paper revisits the classical concept of network modularity and its spectral relaxations used throughout graph data analysis. We formulate and study several modularity statisti…
An Overview of Asymptotic Normality in Stochastic Blockmodels: Cluster Analysis and Inference
Joshua Agterberg, Joshua Cape
This paper provides a selective review of the statistical network analysis literature focused on clustering and inference problems for stochastic blockmodels and their variants. We…
A statistical framework for GWAS of high dimensional phenotypes using summary statistics, with application to metabolite GWAS
Weiqiong Huang, Emily C. Hector, Joshua Cape +1
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…
Spectral embedding and the latent geometry of multipartite networks
Alexander Modell, Ian Gallagher, Joshua Cape +1
Spectral embedding finds vector representations of the nodes of a network, based on the eigenvectors of a properly constructed matrix, and has found applications throughout science…
Multiple Network Embedding for Anomaly Detection in Time Series of Graphs
Guodong Chen, Jesús Arroyo, Avanti Athreya +7
This paper considers the graph signal processing problem of anomaly detection in time series of graphs. We examine two related, complementary inference tasks: the detection of anom…
On spectral algorithms for community detection in stochastic blockmodel graphs with vertex covariates
Cong Mu, Angelo Mele, Lingxin Hao +3
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
Angelo Mele, Lingxin Hao, Joshua Cape +1
In many applications of network analysis, it is important to distinguish between observed and unobserved factors affecting network structure. To this end, we develop spectral estim…
Inference for multiple heterogeneous networks with a common invariant subspace
Jesús Arroyo, Avanti Athreya, Joshua Cape +3
The development of models for multiple heterogeneous network data is of critical importance both in statistical network theory and across multiple application domains. Although sin…
On spectral embedding performance and elucidating network structure in stochastic block model graphs
Joshua Cape, Minh Tang, Carey E. Priebe
Statistical inference on graphs often proceeds via spectral methods involving low-dimensional embeddings of matrix-valued graph representations, such as the graph Laplacian or adja…
On a 'Two Truths' Phenomenon in Spectral Graph Clustering
Carey E. Priebe, Youngser Park, Joshua T. Vogelstein +6
Clustering is concerned with coherently grouping observations without any explicit concept of true groupings. Spectral graph clustering - clustering the vertices of a graph based o…
Bayesian Estimation of Sparse Spiked Covariance Matrices in High Dimensions
Fangzheng Xie, Yanxun Xu, Carey E. Priebe +1
We propose a Bayesian methodology for estimating spiked covariance matrices with jointly sparse structure in high dimensions. The spiked covariance matrix is reparametrized in term…
Signal-plus-noise matrix models: eigenvector deviations and fluctuations
Joshua Cape, Minh Tang, Carey E. Priebe
Estimating eigenvectors and low-dimensional subspaces is of central importance for numerous problems in statistics, computer science, and applied mathematics. This paper characteri…
Asymptotically efficient estimators for stochastic blockmodels: the naive MLE, the rank-constrained MLE, and the spectral
Minh Tang, Joshua Cape, Carey E. Priebe
We establish asymptotic normality results for estimation of the block probability matrix in stochastic blockmodel graphs using spectral embedding when the average degr…
A statistical interpretation of spectral embedding: the generalised random dot product graph
Patrick Rubin-Delanchy, Joshua Cape, Minh Tang +1
Spectral embedding is a procedure which can be used to obtain vector representations of the nodes of a graph. This paper proposes a generalisation of the latent position network mo…
The two-to-infinity norm and singular subspace geometry with applications to high-dimensional statistics
Joshua Cape, Minh Tang, Carey E. Priebe
The singular value matrix decomposition plays a ubiquitous role throughout statistics and related fields. Myriad applications including clustering, classification, and dimensionali…
The Kato--Temple inequality and eigenvalue concentration with applications to graph inference
Joshua Cape, Minh Tang, Carey E. Priebe
We present an adaptation of the Kato--Temple inequality for bounding perturbations of eigenvalues with applications to statistical inference for random graphs, specifically hypothe…
Symplectic reduction at zero angular momentum
Joshua Cape, Hans-Christian Herbig, Christopher Seaton
We study the symplectic reduction of the phase space describing particles in with total angular momentum zero. This corresponds to the singular symplectic quotie…