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Forecasting Models for Graph Processes: A Study on the Multi-Dimensional Case

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 multi-dimensional graph processes, i.e.,...

master thesis 2022

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Graph filter designs and implementations

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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Side-channel analysis with graph neural networks

de Bruijn, Vasco (author)

In cyber security, side-channel 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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Graphon Filters: Graph Signal Processing in the Limit

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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Accuracy-diversity trade-off in recommender systems via graph convolutions

Isufi, E. (author), Pocchiari, Matteo (author), Hanjalic, A. (author)

Graph convolutions, in both their linear and neural network forms, have reached state-of-the-art accuracy on recommender system (RecSys) benchmarks. However, recommendation accuracy is tied with diversity in a delicate trade-off 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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The Dual Graph Shift Operator: Identifying the Support of the Frequency Domain

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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Graph-time convolutional neural networks

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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Nonlinear State-Space Generalizations of Graph Convolutional Neural Networks

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 state-space perspective revealing that the graph convolutional module is a minimalistic linear state-space model, in which the state update matrix is...

conference paper 2021

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Topological Volterra Filters

Leus, G.J.T. (author), Yang, Maosheng (author), Coutino, Mario (author), Isufi, E. (author)

To deal with high-dimensional 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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Sampling Graph Signals with Sparse Dictionary Representation

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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Accuracy-Diversity Trade-off in Recommender Systems Via Graph Convolutions

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 trade-off due to...

master thesis 2020

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Identifying Author Fingerprints in Texts via Graph Neural Networks

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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Advances in Graph Signal Processing: Fast graph construction & Node-adaptive graph signal reconstruction

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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State-space based network topology identification

Coutino, Mario (author), Isufi, E. (author), Maehara, T. (author), Leus, G.J.T. (author)

In this work, we explore the state-space formulation of network processes to recover the underlying network structure (local connections). To do so, we employ subspace techniques borrowed from system identification literature and extend them to the network topology inference problem. This approach provides a unified view of the traditional...

conference paper 2020

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Privacy-Preserving Distributed Graph Filtering

Li, Qiongxiu (author), Coutino, Mario (author), Leus, G.J.T. (author), Christensen, M. Graesboll (author)

With an increasingly interconnected and digitized world, distributed signal processing and graph signal processing have been proposed to process its big amount of data. However, privacy has become one of the biggest challenges holding back the widespread adoption of these tools for processing sensitive data. As a step towards a solution, we...

conference paper 2020

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Graph-time spectral analysis for atrial fibrillation

Sun, M. (author), Isufi, E. (author), de Groot, N.M.S. (author), Hendriks, R.C. (author)

Atrial fibrillation is a clinical arrhythmia with multifactorial mechanisms still unresolved. Time-frequency analysis of epicardial electrograms has been investigated to study atrial fibrillation. However, deeper understanding can be achieved by incorporating the spatial dimension. Unfortunately, the physical models describing the spatial...

journal article 2020

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Observing and tracking bandlimited graph processes from sampled measurements

Isufi, E. (author), Banelli, Paolo (author), Di Lorenzo, Paolo (author), Leus, G.J.T. (author)

A critical challenge in graph signal processing is the sampling of bandlimited graph signals; signals that are sparse in a well-defined graph Fourier domain. Current works focused on sampling time-invariant graph signals and ignored their temporal evolution. However, time can bring new insights on sampling since sensor, biological, and...

journal article 2020

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Fast spectral approximation of structured graphs with applications to graph filtering

Coutino, Mario (author), Chepuri, Sundeep Prabhakar (author), Maehara, Takanori (author), Leus, G.J.T. (author)

To analyze and synthesize signals on networks or graphs, Fourier theory has been extended to irregular domains, leading to a so-called graph Fourier transform. Unfortunately, different from the traditional Fourier transform, each graph exhibits a different graph Fourier transform. Therefore to analyze the graph-frequency domain properties of...

journal article 2020

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Node varying regularization for graph signals

Yang, M. (author), Coutino, Mario (author), Isufi, E. (author), Leus, G.J.T. (author)

While regularization on graphs has been successful for signal reconstruction, strategies for controlling the bias-variance trade-off of such methods have not been completely explored. In this work, we put forth a node varying regularizer for graph signal reconstruction and develop a minmax approach to design the vector of regularization...

conference paper 2020

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Topology-Aware Joint Graph Filter and Edge Weight Identification for Network Processes

Natali, A. (author), Coutino, Mario (author), Leus, G.J.T. (author)

Data defined over a network have been successfully modelled by means of graph filters. However, although in many scenarios the connectivity of the network is known, e.g., smart grids, social networks, etc., the lack of well-defined interaction weights hinders the ability to model the observed networked data using graph filters. Therefore, in...

conference paper 2020