Search Results for author: Avanti Athreya

Found 10 papers, 4 papers with code

Multiple Network Embedding for Anomaly Detection in Time Series of Graphs

1 code implementation23 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.


The Importance of Being Correlated: Implications of Dependence in Joint Spectral Inference across Multiple Networks

no code implementations1 Aug 2020 Konstantinos Pantazis, Avanti Athreya, Jesús Arroyo, William N. Frost, Evan S. Hill, Vince Lyzinski

We describe how this omnibus embedding can itself induce correlation, leading us to distinguish between inherent correlation -- the correlation that arises naturally in multisample network data -- and induced correlation, which is an artifice of the joint embedding methodology.

Time Series

On spectral algorithms for community detection in stochastic blockmodel graphs with vertex covariates

1 code implementation4 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.

Community Detection

Statistical inference on random dot product graphs: a survey

no code implementations16 Sep 2017 Avanti Athreya, Donniell E. Fishkind, Keith Levin, Vince Lyzinski, Youngser Park, Yichen Qin, Daniel L. Sussman, Minh Tang, Joshua T. Vogelstein, Carey E. Priebe

In this survey paper, we describe a comprehensive paradigm for statistical inference on random dot product graphs, a paradigm centered on spectral embeddings of adjacency and Laplacian matrices.

Community Detection

Perfect Clustering for Stochastic Blockmodel Graphs via Adjacency Spectral Embedding

no code implementations2 Oct 2013 Vince Lyzinski, Daniel Sussman, Minh Tang, Avanti Athreya, Carey Priebe

Vertex clustering in a stochastic blockmodel graph has wide applicability and has been the subject of extensive research.

A central limit theorem for scaled eigenvectors of random dot product graphs

no code implementations31 May 2013 Avanti Athreya, Vince Lyzinski, David J. Marchette, Carey E. Priebe, Daniel L. Sussman, Minh Tang

We prove a central limit theorem for the components of the largest eigenvectors of the adjacency matrix of a finite-dimensional random dot product graph whose true latent positions are unknown.

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