Joshua Cape
Biographic Data
| ID | 4163898 |
|---|---|
| NAME | Joshua Cape |
| GIVEN NAMES | Joshua |
| FAMILY NAME | Cape |
| SIGNATURE | CAPE J |
| AFFILIATIONS | University of Wisconsin–Madison |
| ORCID | 0000-0002-1471-1650 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2019 |
| LATEST PUBLICATION YEAR | 2023 |
| H-INDEX | 0 |
Spectral Estimation of Large Stochastic Blockmodels with Discrete Nodal Covariates
In many applications of network analysis, it is important to distinguish between observed and unobserved factors affecting network structure. We show that a network model with discrete unobserved link heterogeneity and binary (or discrete) covariates corresponds to a stochastic blockmodel (SBM). We develop a spectral estimator for the effect of covariates on link probabilities, exploiting the correspondence of SBMs and generalized random dot prod…
On spectral embedding performance and elucidating network structure in stochastic blockmodel graphs
Statistical inference on graphs often proceeds via spectral methods involving low-dimensional embeddings of matrix-valued graph representations such as the graph Laplacian or adjacency matrix. In this paper, we analyze the asymptotic information-theoretic relative performance of Laplacian spectral embedding and adjacency spectral embedding for block assignment recovery in stochastic blockmodel graphs by way of Chernoff information. We investigate…
No prominent works on this page.
On spectral embedding performance and elucidating network structure in stochastic blockmodel graphs
Statistical inference on graphs often proceeds via spectral methods involving low-dimensional embeddings of matrix-valued graph representations such as the graph Laplacian or adjacency matrix. In this paper, we analyze the asymptotic information-theoretic relative performance of Laplacian spectral embedding and adjacency spectral embedding for block assignment recovery in stochastic blockmodel graphs by way of Chernoff information. We investigate…
Spectral Estimation of Large Stochastic Blockmodels with Discrete Nodal Covariates
In many applications of network analysis, it is important to distinguish between observed and unobserved factors affecting network structure. We show that a network model with discrete unobserved link heterogeneity and binary (or discrete) covariates corresponds to a stochastic blockmodel (SBM). We develop a spectral estimator for the effect of covariates on link probabilities, exploiting the correspondence of SBMs and generalized random dot prod…
Mathematics (2 works) · Adjacency list (1 work) · Adjacency matrix (1 work) · Applied Mathematics (1 work) · Artificial Intelligence (1 work) · Bayesian Methods and Mixture Models (1 work) · Block (permutation group theory (1 work) · Combinatorics (1 work) · Complex Network Analysis Techniques (1 work) · Computer Science (1 work)