We study a blind deconvolution problem on graphs, which arises in the context of localizing
a few sources that diffuse over networks. While the observations are bilinear functions of the …

This paper investigates the recovery of a node-domain sparse graph signal from the output
of a graph filter. This problem, which is often referred to as the identification of the source of …

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 …

We study blind deconvolution of signals defined on the nodes of an undirected graph.
Although observations are bilinear functions of both unknowns, namely the forward …

Graph matching over two given graphs is a well-established method for re-identifying
obscured node labels within an anonymous graph by matching the corresponding nodes in …

In this paper, we incorporate a graph filter deconvolution step into the classical geometric
convolutional neural network pipeline. More precisely, under the assumption that the graph …

This paper investigates the recovery of a node-domain sparse graph signal from the output
of a graph filter. This problem, often referred to as the identification of the source of a diffused …

We present a novel model-based deep learning solution for the inverse problem of
localizing sources of network diffusion. Starting from first graph signal processing (GSP) …

Blind deconvolution over graphs involves using (observed) output graph signals to obtain
both the inputs (sources) as well as the filter that drives (models) the graph diffusion process …

We propose a deep learning solution to the inverse problem of localizing sources of network
diffusion. Invoking graph signal processing (GSP) fundamentals, the problem boils down to …