no code implementations • 16 Sep 2017 • Patrick Rubin-Delanchy, Joshua Cape, Minh Tang, Carey E. Priebe
Spectral embedding is a procedure which can be used to obtain vector representations of the nodes of a graph.
no code implementations • 23 Aug 2018 • Carey E. Priebe, Youngser Park, Joshua T. Vogelstein, John M. Conroy, Vince Lyzinski, Minh Tang, Avanti Athreya, Joshua Cape, Eric Bridgeford
Clustering is concerned with coherently grouping observations without any explicit concept of true groupings.
no code implementations • 18 Aug 2019 • Angelo Mele, Lingxin Hao, Joshua Cape, Carey E. Priebe
In many applications of network analysis, it is important to distinguish between observed and unobserved factors affecting network structure.
1 code implementation • 4 Jul 2020 • Cong Mu, Angelo Mele, Lingxin Hao, Joshua Cape, Avanti Athreya, Carey E. Priebe
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.
1 code implementation • 23 Aug 2020 • Guodong Chen, Jesús Arroyo, Avanti Athreya, Joshua Cape, Joshua T. Vogelstein, Youngser Park, Chris White, Jonathan Larson, Weiwei Yang, Carey E. Priebe
We examine two related, complementary inference tasks: the detection of anomalous graphs within a time series, and the detection of temporally anomalous vertices.
Methodology
1 code implementation • 8 Feb 2022 • Alexander Modell, Ian Gallagher, Joshua Cape, Patrick Rubin-Delanchy
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.
no code implementations • 17 Mar 2023 • Weiqiong Huang, Emily C. Hector, Joshua Cape, Chris McKennan
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.