research
∙
05/13/2022
Discovering underlying dynamics in time series of networks
Understanding dramatic changes in the evolution of networks is central t...
share
research
∙
03/26/2021
Beyond the adjacency matrix: random line graphs and inference for networks with edge attributes
Any modern network inference paradigm must incorporate multiple aspects ...
research
∙
08/23/2020
Multiple Network Embedding for Anomaly Detection in Time Series of Graphs
This paper considers the graph signal processing problem of anomaly dete...
research
∙
08/01/2020
The Importance of Being Correlated: Implications of Dependence in Joint Spectral Inference across Multiple Networks
Spectral inference on multiple networks is a rapidly-developing subfield...
research
∙
07/04/2020
On identifying unobserved heterogeneity in stochastic blockmodel graphs with vertex covariates
Both observed and unobserved vertex heterogeneity can influence block st...
research
∙
05/20/2020
Learning to rank via combining representations
Learning to rank – producing a ranked list of items specific to a query ...
research
∙
02/23/2020
On a complete and sufficient statistic for the correlated Bernoulli random graph model
Inference on vertex-aligned graphs is of wide theoretical and practical ...
research
∙
06/24/2019
Inference for multiple heterogeneous networks with a common invariant subspace
The development of models for multiple heterogeneous network data is of ...
research
∙
08/23/2018
On a 'Two Truths' Phenomenon in Spectral Graph Clustering
Clustering is concerned with coherently grouping observations without an...
research
∙
06/04/2018
On estimation and inference in latent structure random graphs
We define a latent structure model (LSM) random graph as a random dot pr...
research
∙
09/16/2017
Statistical inference on random dot product graphs: a survey
The random dot product graph (RDPG) is an independent-edge random graph ...
research
∙
05/09/2017
Semiparametric spectral modeling of the Drosophila connectome
We present semiparametric spectral modeling of the complete larval Droso...
research
∙
03/07/2015
Community Detection and Classification in Hierarchical Stochastic Blockmodels
We propose a robust, scalable, integrated methodology for community dete...
research
∙
10/02/2013
Perfect Clustering for Stochastic Blockmodel Graphs via Adjacency Spectral Embedding
Vertex clustering in a stochastic blockmodel graph has wide applicabilit...
research
∙
05/31/2013