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Liu, Chengen (author), Leus, G.J.T. (author), Isufi, E. (author)
The edge flow reconstruction task consists of retreiving edge flow signals from corrupted or incomplete measurements. This is typically solved by a regularized optimization problem on higher-order networks such as simplicial complexes and the corresponding regularizers are chosen based on prior knowledge. Tailoring this prior to the setting...
journal article 2023
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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Yang, Maosheng (author), Isufi, E. (author), Schaub, Michael T. (author), Leus, G.J.T. (author)
We study linear filters for processing signals supported on abstract topological spaces modeled as simplicial complexes, which may be interpreted as generalizations of graphs that account for nodes, edges, triangular faces, etc. To process such signals, we develop simplicial convolutional filters defined as matrix polynomials of the lower and...
journal article 2022
document
Yang, Maosheng (author), Isufi, E. (author), Leus, G.J.T. (author)
Graphs can model networked data by representing them as nodes and their pairwise relationships as edges. Recently, signal processing and neural networks have been extended to process and learn from data on graphs, with achievements in tasks like graph signal reconstruction, graph or node classifications, and link prediction. However, these...
conference paper 2022
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He, Y. (author), Coutino, Mario (author), Isufi, E. (author), Leus, G.J.T. (author)
In this work, we focus on partitioning dynamic graphs with two types of nodes (bi-colored), though not necessarily bipartite graphs. They commonly appear in communication network applications, e.g., one color being base stations, the other users, and the dynamic process being the varying connection status between base stations and moving...
conference paper 2022
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Yang, Maosheng (author), Coutino, Mario (author), Leus, G.J.T. (author), Isufi, E. (author)
A critical task in graph signal processing is to estimate the true signal from noisy observations over a subset of nodes, also known as the reconstruction problem. In this paper, we propose a node-adaptive regularization for graph signal reconstruction, which surmounts the conventional Tikhonov regularization, giving rise to more degrees of...
journal article 2021
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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
document
Natali, A. (author), Coutino, Mario (author), Isufi, E. (author), Leus, G.J.T. (author)
Signal processing and machine learning algorithms for data sup-ported over graphs, require the knowledge of the graph topology. Unless this information is given by the physics of the problem (e.g., water supply networks, power grids), the topology has to be learned from data. Topology identification is a challenging task, as the problem is often...
conference paper 2021
document
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
document
Yang, Maosheng (author), Isufi, E. (author), Schaub, Michael T. (author), Leus, G.J.T. (author)
In this paper, we study linear filters to process signals defined on simplicial complexes, i.e., signals defined on nodes, edges, triangles, etc. of a simplicial complex, thereby generalizing filtering operations for graph signals. We propose a finite impulse response filter based on the Hodge Laplacian, and demonstrate how this filter can be...
conference paper 2021
document
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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Gama, F. (author), Isufi, E. (author), Leus, G.J.T. (author), Ribeiro, Alejandro (author)
Network data can be conveniently modeled as a graph signal, where data values are assigned to nodes of a graph that describes the underlying network topology. Successful learning from network data is built upon methods that effectively exploit this graph structure. In this article, we leverage graph signal processing (GSP) to characterize the...
review 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
Das, Bishwadeep (author), Isufi, E. (author), Leus, G.J.T. (author)
Diffusion-based semi-supervised learning on graphs consists of diffusing labeled information of a few nodes to infer the labels on the remaining ones. The performance of these methods heavily relies on the initial labeled set, which is either generated randomly or using heuristics. The first sometimes leads to unsatisfactory results because...
conference paper 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
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
document
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
document
Gama, F. (author), Isufi, E. (author), Ribeiro, Alejandro (author), Leus, G.J.T. (author)
Controllability of complex networks arises in many technological problems involving social, financial, road, communication, and smart grid networks. In many practical situations, the underlying topology might change randomly with time, due to link failures such as changing friendships, road blocks or sensor malfunctions. Thus, it leads to...
journal article 2019
document
Isufi, E. (author), Banelli, Paolo (author), Di Lorenzo, Paolo (author), Leus, G.J.T. (author)
This work merges tools from graph signal processing and linear systems theory to propose sampling strategies for observing the initial state of a process evolving over a graph. The proposed method is ratified by a mathematical analysis that provides insights on the role played by the different actors, such as the graph topology, the process...
conference paper 2019
document
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
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