21 citations · 29 across the 9 of their papers we have counts for
11 papers · 1 filter
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…
Graph-signal Reconstruction and Blind Deconvolution for Structured Inputs
David Ramírez, Antonio G. Marques, Santiago Segarra
Key to successfully deal with complex contemporary datasets is the development of tractable models that account for the irregular structure of the information at hand. This paper p…
Robust graph-filter identification with graph denoising regularization
Samuel Rey, Antonio G. Marques
When approaching graph signal processing tasks, graphs are usually assumed to be perfectly known. However, in many practical applications, the observed (inferred) network is prone…
Signal Processing on Directed Graphs
Antonio G. Marques, Santiago Segarra, Gonzalo Mateos
This paper provides an overview of the current landscape of signal processing (SP) on directed graphs (digraphs). Directionality is inherent to many real-world (information, transp…
Generative Adversarial Networks For Graph Data Imputation From Signed Observations
Amarlingam Madapu, Santiago Segarra, Sundeep Prabhakar Chepuri +1
We study the problem of missing data imputation for graph signals from signed one-bit quantized observations. More precisely, we consider that the true graph data is drawn from a d…
Estimating Network Processes via Blind Identification of Multiple Graph Filters
Yu Zhu, Fernando J. Iglesias, Antonio G. Marques +1
This paper studies the problem of jointly estimating multiple network processes driven by a common unknown input, thus effectively generalizing the classical blind multi-channel id…