Searched for: subject%3A%22Graph%255C%2Bsignal%255C%2Bprocessing%22
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van der Hoeven, Jelmer (author)In the current Big Data era, large amounts of data are collected from complex systems, such as sensor networks and social networks. The emerging field of graph signal processing (GSP) leverages a network structure (graph) to process signals on an irregular domain. This thesis studies the forecasting of multidimensional graph processes, i.e.,...master thesis 2022
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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
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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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Natali, A. (author), Isufi, E. (author), Coutino, Mario (author), Leus, G.J.T. (author)This work proposes an algorithmic framework to learn timevarying graphs from online data. The generality offered by the framework renders it modelindependent, i.e., it can be theoretically analyzed in its abstract formulation and then instantiated under a variety of modeldependent graph learning problems. This is possible by phrasing (time...journal article 2022
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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 illconditioning of the involved system matrix. This issue, accentuated for increasing graph filter orders,...journal article 2022
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Liu, J. (author)The ability to model irregular data and the interactions between them have<br/>extended the traditional signal processing tools to the graph domain. Under<br/>these circumstances, the emergence of graph signal processing has offered a<br/>brand new framework for dealing with complex data. In particular, the graph<br/>Fourier transform (GFT) lets...doctoral thesis 2021
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de Bruijn, Vasco (author)In cyber security, sidechannel attacks (SCA) are of interest because they target the vulnerabilities in implementation rather than inherent vulnerabilities in the algorithm. Profiled SCA is especially interesting as it assumes that the adversary has unlimited access to a clone device that can generate sufficient traces to create a profile of...master thesis 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
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Isufi, E. (author), Mazzola, Gabriele (author)Spatiotemporal data can be represented as a process over a graph, which captures their spatial relationships either explicitly or implicitly. How to leverage such a structure for learning representations is one of the key challenges when working with graphs. In this paper, we represent the spatiotemporal relationships through product graphs...conference paper 2021
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Zhang, Kaiwen (author), Coutino, Mario (author), Isufi, E. (author)Graph sampling strategies require the signal to be relatively sparse in an alternative domain, e.g. bandlimitedness for reconstructing the signal. When such a condition is violated or its approximation demands a large bandwidth, the reconstruction often comes with unsatisfactory results even with large samples. In this paper, we propose an...conference paper 2021
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Isufi, E. (author), Pocchiari, Matteo (author), Hanjalic, A. (author)Graph convolutions, in both their linear and neural network forms, have reached stateoftheart accuracy on recommender system (RecSys) benchmarks. However, recommendation accuracy is tied with diversity in a delicate tradeoff and the potential of graph convolutions to improve the latter is unexplored. Here, we develop a model that learns...journal article 2021
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Morency, M.W. (author), Leus, G.J.T. (author)Graph signal processing is an emerging field which aims to model processes that exist on the nodes of a network and are explained through diffusion over this structure. Graph signal processing works have heretofore assumed knowledge of the graph shift operator. Our approach is to investigate the question of graph filtering on a graph about...journal article 2021
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Ruiz, Luana (author), Gama, Fernando (author), Ribeiro, Alejandro (author), Isufi, E. (author)Graph convolutional neural networks (GCNNs) learn compositional representations from network data by nesting linear graph convolutions into nonlinearities. In this work, we approach GCNNs from a statespace perspective revealing that the graph convolutional module is a minimalistic linear statespace model, in which the state update matrix is...conference paper 2021
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Leus, G.J.T. (author), Yang, M. (author), Coutino, Mario (author), Isufi, E. (author)To deal with highdimensional data, graph filters have shown their power in both graph signal processing and data science. However, graph filters process signals exploiting only pairwise interactions between the nodes, and they are not able to exploit more complicated topological structures. Graph Volterra models, on the other hand, are also...conference paper 2021
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Leus, G.J.T. (author), Segarra, Santiago (author), Ribeiro, Alejandro (author), Marques, Antonio G. (author)Contemporary data is often supported by an irregular structure, which can be conveniently captured by a graph. Accounting for this graph support is crucial to analyze the data, leading to an area known as graph signal processing (GSP). The two most important tools in GSP are the graph shift operator (GSO), which is a sparse matrix accounting...journal article 2021
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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 timevarying, rendering the problem even harder. In this paper, we focus on a timevarying version of the structural...conference paper 2021
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Pocchiari, M. (author)Recommender Systems assist the user by suggesting items to be consumed based on the user's history. The topic of diversity in recommendation gained momentum in recent years as additional criterion besides recommendation accuracy, to improve user satisfaction. Accuracy and diversity in recommender systems coexist in a delicate tradeoff due to...master thesis 2020
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Sipko, Tomas (author)The world is generating more and more network data in many different areas (e.g., sensor networks, social networks and even text). A unique characteristic of these data is the coupling between data values and underlying irregular structure on which these values are defined. Thus, researchers developed Graph Neural Networks (GNNs) to use deep...master thesis 2020
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Yang, Maosheng (author)This thesis consists of two parts in both data science and signal processing over graphs. In the first part of this thesis, we aim to solve the problem of graph construction in big data scenario, which is critical for practical tasks, like collaborative filtering in recommender systems, spectral embedding or clustering in learning algorithms. We...master thesis 2020
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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 wavefront 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
Searched for: subject%3A%22Graph%255C%2Bsignal%255C%2Bprocessing%22
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