1 citations · 1 across the 6 of their papers we have counts for
8 papers
Online Network Inference from Graph-Stationary Signals with Hidden Nodes
Andrei Buciulea, Madeline Navarro, Samuel Rey +2
Graph learning is the fundamental task of estimating unknown graph connectivity from available data. Typical approaches assume that not only is all information available simultaneo…
Redesigning graph filter-based GNNs to relax the homophily assumption
Samuel Rey, Madeline Navarro, Victor M. Tenorio +2
Graph neural networks (GNNs) have become a workhorse approach for learning from data defined over irregular domains, typically by implicitly assuming that the data structure is rep…
Joint graph learning from Gaussian observations in the presence of hidden nodes
Samuel Rey, Madeline Navarro, Andrei Buciulea +2
Graph learning problems are typically approached by focusing on learning the topology of a single graph when signals from all nodes are available. However, many contemporary setups…
GraphMAD: Graph Mixup for Data Augmentation using Data-Driven Convex Clustering
Madeline Navarro, Santiago Segarra
We develop a novel data-driven nonlinear mixup mechanism for graph data augmentation and present different mixup functions for sample pairs and their labels. Mixup is a data augmen…
Graphon-aided Joint Estimation of Multiple Graphs
Madeline Navarro, Santiago Segarra
We consider the problem of estimating the topology of multiple networks from nodal observations, where these networks are assumed to be drawn from the same (unknown) random graph m…
Network Clustering for Latent State and Changepoint Detection
Madeline Navarro, Genevera I. Allen, Michael Weylandt
Network models provide a powerful and flexible framework for analyzing a wide range of structured data sources. In many situations of interest, however, multiple networks can be co…