Search Results for author: Jesús Arroyo

Found 10 papers, 7 papers with code

Overlapping community detection in networks via sparse spectral decomposition

1 code implementation20 Sep 2020 Jesús Arroyo, Elizaveta Levina

We consider the problem of estimating overlapping community memberships in a network, where each node can belong to multiple communities.

Clustering Community Detection +2

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 Analysis

Simultaneous prediction and community detection for networks with application to neuroimaging

1 code implementation5 Feb 2020 Jesús Arroyo, Elizaveta Levina

Here we present a method for supervised community detection, aiming to find a partition of the network into communities that is most useful for predicting a particular response.

Clustering Community Detection +1

Graph matching between bipartite and unipartite networks: to collapse, or not to collapse, that is the question

1 code implementation5 Feb 2020 Jesús Arroyo, Carey E. Priebe, Vince Lyzinski

Graph matching consists of aligning the vertices of two unlabeled graphs in order to maximize the shared structure across networks; when the graphs are unipartite, this is commonly formulated as minimizing their edge disagreements.

Graph Matching

Random Forests for Adaptive Nearest Neighbor Estimation of Information-Theoretic Quantities

1 code implementation30 Jun 2019 Ronan Perry, Ronak Mehta, Richard Guo, Eva Yezerets, Jesús Arroyo, Mike Powell, Hayden Helm, Cencheng Shen, Joshua T. Vogelstein

Information-theoretic quantities, such as conditional entropy and mutual information, are critical data summaries for quantifying uncertainty.

Maximum Likelihood Estimation and Graph Matching in Errorfully Observed Networks

no code implementations26 Dec 2018 Jesús Arroyo, Daniel L. Sussman, Carey E. Priebe, Vince Lyzinski

Given a pair of graphs with the same number of vertices, the inexact graph matching problem consists in finding a correspondence between the vertices of these graphs that minimizes the total number of induced edge disagreements.

Graph Matching

Joint Embedding of Graphs

2 code implementations10 Mar 2017 Shangsi Wang, Jesús Arroyo, Joshua T. Vogelstein, Carey E. Priebe

Feature extraction and dimension reduction for networks is critical in a wide variety of domains.

Dimensionality Reduction

Efficient Distributed Estimation of Inverse Covariance Matrices

no code implementations3 May 2016 Jesús Arroyo, Elizabeth Hou

In distributed systems, communication is a major concern due to issues such as its vulnerability or efficiency.

Model Selection

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