activity
20172025
most citedSimultaneous prediction and community detection for networks with application to neuroimaging

2 citations · 4 across the 6 of their papers we have counts for

collaborators
Showing 2020Show all

6 papers · 1 filter

q-bio.NC2020

Multiscale Comparative Connectomics

Vivek Gopalakrishnan, Jaewon Chung, Eric Bridgeford +8

The connectome, a map of the structural and/or functional connections in the brain, provides a complex representation of the neurobiological phenotypes on which it supervenes. This…

cs.SI2020

Overlapping community detection in networks via sparse spectral decomposition

Jesús Arroyo, Elizaveta Levina

We consider the problem of estimating overlapping community memberships in a network, where each node can belong to multiple communities. More than a few communities per node are d…

stat.ME2020

Multiple Network Embedding for Anomaly Detection in Time Series of Graphs

Guodong Chen, Jesús Arroyo, Avanti Athreya +7

This paper considers the graph signal processing problem of anomaly detection in time series of graphs. We examine two related, complementary inference tasks: the detection of anom…

stat.ME2020

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

Konstantinos Pantazis, Avanti Athreya, Jesús Arroyo +3

Spectral inference on multiple networks is a rapidly-developing subfield of graph statistics. Recent work has demonstrated that joint, or simultaneous, spectral embedding of multip…

stat.ME2020★ 2 cited

Simultaneous prediction and community detection for networks with application to neuroimaging

Jesús Arroyo, Elizaveta Levina

Community structure in networks is observed in many different domains, and unsupervised community detection has received a lot of attention in the literature. Increasingly the focu…

stat.ML2020

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

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…