Searched for: subject%3A%22process%22
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Di Lorenzo, Paolo (author), Banelli, Paolo (author), Isufi, E. (author), Barbarossa, Sergio (author), Leus, G.J.T. (author)
The goal of this paper is to propose novel strategies for adaptive learning of signals defined over graphs, which are observed over a (randomly) time-varying subset of vertices. We recast two classical adaptive algorithms in the graph signal processing framework, namely, the least mean squares (LMS) and the recursive least squares (RLS)...
journal article 2018
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Coutino, Mario (author), Isufi, E. (author), Leus, G.J.T. (author)
The main challenges distributed graph filters face in practice are the communication overhead and computational complexity. In this work, we extend the state-of-the-art distributed finite impulse response (FIR) graph filters to an edge-variant (EV) version, i.e., a filter where every node weights the signals from its neighbors with different...
conference paper 2018
document
Isufi, E. (author), Mahabir, Ashvant S.U. (author), Leus, G.J.T. (author)
This letter investigates methods to detect graph topological changes without making any assumption on the nature of the change itself. To accomplish this, we merge recently developed tools in graph signal processing with matched subspace detection theory and propose two blind topology change detectors. The first detector exploits the prior...
journal article 2018
document
Gama, F. (author), Marques, Antonio G. (author), Ribeiro, Alejandro (author), Leus, G.J.T. (author)
Superior performance and ease of implementation have fostered the adoption of Convolutional Neural Networks (CNN s) for a wide array of inference and reconstruction tasks. CNNs implement three basic blocks: convolution, pooling and pointwise nonlinearity. Since the two first operations are well-defined only on regular-structured data such as...
conference paper 2018
document
Gama, F. (author), Leus, G.J.T. (author), Marques, Antonio G. (author), Ribeiro, Alejandro (author)
Convolutional neural networks (CNNs) are being applied to an increasing number of problems and fields due to their superior performance in classification and regression tasks. Since two of the key operations that CNNs implement are convolution and pooling, this type of networks is implicitly designed to act on data described by regular...
conference paper 2018
document
Gama, F. (author), Marques, Antonio G. (author), Leus, G.J.T. (author), Ribeiro, Alejandro (author)
Two architectures that generalize convolutional neural networks (CNNs) for the processing of signals supported on graphs are introduced. We start with the selection graph neural network (GNN), which replaces linear time invariant filters with linear shift invariant graph filters to generate convolutional features and reinterprets pooling as a...
journal article 2019
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Gama, Fernando (author), Marques, Antonio G. (author), Leus, G.J.T. (author), Ribeiro, Alejandro (author)
Convolutional neural networks (CNNs) restrict the, otherwise arbitrary, linear operation of neural networks to be a convolution with a bank of learned filters. This makes them suitable for learning tasks based on data that exhibit the regular structure of time signals and images. The use of convolutions, however, makes them unsuitable for...
conference paper 2019
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Coutino, Mario (author), Isufi, E. (author), Leus, G.J.T. (author)
Graph filters are one of the core tools in graph signal processing. A central aspect of them is their direct distributed implementation. However, the filtering performance is often traded with distributed communication and computational savings. To improve this tradeoff, this paper generalizes state-of-the-art distributed graph filters to...
journal article 2019
document
Gama, F. (author), Marques, Antonio G. (author), Ribeiro, Alejandro (author), Leus, G.J.T. (author)
Graph neural networks (GNNs) regularize classical neural networks by exploiting the underlying irregular structure supporting graph data, extending its application to broader data domains. The aggregation GNN presented here is a novel GNN that exploits the fact that the data collected at a single node by means of successive local exchanges...
conference paper 2019
document
Coutino, Mario (author), Leus, G.J.T. (author)
As the size of the sensor network grows, synchronization starts to become the main bottleneck for distributed computing. As a result, efforts in several areas have been focused on the convergence analysis of asynchronous computational methods. In this work, we aim to cross-pollinate distributed graph filters with results in parallel computing...
conference paper 2019
document
Rimleanscaia, Oxana (author), Isufi, E. (author)
This paper proposes rational Chebyshev graph filters to approximate step graph spectral responses with arbitrary precision, which are of interest in graph filter banks and spectral clustering. The proposed method relies on the well-known Chebyshev filters of the first kind and on a domain transform of the angular frequencies to the graph...
conference paper 2020
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Coutino, Mario (author), Isufi, E. (author), Maehara, Takanori (author), Leus, G.J.T. (author)
In this article, we explore the state-space formulation of a network process to recover from partial observations the network topology that drives its dynamics. To do so, we employ subspace techniques borrowed from system identification literature and extend them to the network topology identification problem. This approach provides a unified...
journal article 2020
document
Natali, A. (author), Isufi, E. (author), Leus, G.J.T. (author)
The forecasting of multi-variate time processes through graph-based techniques has recently been addressed under the graph signal processing framework. However, problems in the representation and the processing arise when each time series carries a vector of quantities rather than a scalar one. To tackle this issue, we devise a new framework and...
conference paper 2020
document
Iancu, A. (author), Isufi, E. (author)
Atrial electrograms are often used to gain understanding on the development of atrial fibrillation (AF). Using such electrograms, cardiologists can reconstruct how the depolarization wave-front propagates across the atrium. Knowing the exact moment at which the depolarization wavefront in the tissue reaches each electrode is an important aspect...
conference paper 2020
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Natali, A. (author), Isufi, E. (author), Coutino, Mario (author), Leus, G.J.T. (author)
Topology identification is an important problem across many disciplines, since it reveals pairwise interactions among entities and can be used to interpret graph data. In many scenarios, however, this (unknown) topology is time-varying, rendering the problem even harder. In this paper, we focus on a time-varying version of the structural...
conference paper 2021
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Coutino, Mario (author)
To the surprise of most of us, complexity in nature spawns from simplicity. No matter how simple a basic unit is, when many of them work together, the interactions among these units lead to complexity. This complexity is present in the spreading of diseases, where slightly different policies, or conditions,might lead to very different results;...
doctoral thesis 2021
document
Isufi, E. (author), Gama, Fernando (author), Ribeiro, Alejandro (author)
Driven by the outstanding performance of neural networks in the structured euclidean domain, recent years have seen a surge of interest in developing neural networks for graphs and data supported on graphs. The graph is leveraged at each layer of the neural network as a parameterization to capture detail at the node level with a reduced...
journal article 2022
document
Coutino, Mario (author), Leus, G.J.T. (author)
One of the main challenges of graph filters is the stability of their design. While classical graph filters allow for a stable design using optimal polynomial approximation theory, generalized graph filters tend to suffer from the ill-conditioning of the involved system matrix. This issue, accentuated for increasing graph filter orders,...
journal article 2022
document
Natali, A. (author), Isufi, E. (author), Coutino, Mario (author), Leus, G.J.T. (author)
This work proposes an algorithmic framework to learn time-varying graphs from online data. The generality offered by the framework renders it model-independent, i.e., it can be theoretically analyzed in its abstract formulation and then instantiated under a variety of model-dependent graph learning problems. This is possible by phrasing (time...
journal article 2022
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Das, B. (author), Hanjalic, A. (author), Isufi, E. (author)
Data processing over graphs is usually done on graphs of fixed size. However, graphs often grow with new nodes arriving over time. Knowing the connectivity information of these nodes, and thus, the expanded graph is crucial for processing data over the expanded graph. In its absence, its inference and the subsequent data processing become...
journal article 2022
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