Searched for: author%3A%22Isufi%2C+E.%22
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Bentivoglio, Roberto (author), Isufi, E. (author), Jonkman, Sebastiaan N. (author), Taormina, R. (author)
Numerical modelling is a reliable tool for flood simulations, but accurate solutions are computationally expensive. In recent years, researchers have explored data-driven methodologies based on neural networks to overcome this limitation. However, most models are only used for a specific case study and disregard the dynamic evolution of the...
journal article 2023
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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
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Kerimov, B. (author), Bentivoglio, Roberto (author), Garzón Díaz, J.A. (author), Isufi, E. (author), Tscheikner-Gratl, Franz (author), Steffelbauer, David Bernhard (author), Taormina, R. (author)
Metamodels accurately reproduce the output of physics-based hydraulic models with a significant reduction in simulation times. They are widely employed in water distribution system (WDS) analysis since they enable computationally expensive applications in the design, control, and optimisation of water networks. Recent machine-learning-based...
journal article 2023
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Gao, Zhan (author), Isufi, E. (author)
Stochastic graph neural networks (SGNNs) are information processing architectures that learn representations from data over random graphs. SGNNs are trained with respect to the expected performance, which comes with no guarantee about deviations of particular output realizations around the optimal expectation. To overcome this issue, we propose...
journal article 2023
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Das, B. (author), Isufi, E. (author)
Current spatiotemporal learning methods for complex data exploit the graph structure as an inductive bias to restrict the function space and improve data and computation efficiency. However, these methods work principally on graphs with a fixed size, whereas in several applications there are expanding graphs where new nodes join the network; e.g...
conference paper 2023
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Yang, Maosheng (author), Das, B. (author), Isufi, E. (author)
Simplicial convolutional filters can process signals defined over levels of a simplicial complex such as nodes, edges, triangles, and so on with applications in e.g., flow prediction in transportation or financial networks. However, the underlying topology expands over time in a way that new edges and triangles form. For example, in a...
conference paper 2023
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Krishnan, Joshin (author), Money, Rohan (author), Beferull-Lozano, Baltasar (author), Isufi, E. (author)
Vector autoregressive (VAR) model is widely used to model time-varying processes, but it suffers from prohibitive growth of the parameters when the number of time series exceeds a few hundreds. We propose a simplicial VAR model to mitigate the curse of dimensionality of the VAR models when the time series are defined over higher-order network...
conference paper 2023
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Sabbaqi, M. (author), Isufi, E. (author)
Reconstructing missing values and removing noise from network-based multivariate time series requires developing graph-time regularizers capable of capturing their spatiotemporal behavior. However, current approaches based on joint spatiotemporal smoothness, diffusion, or variations thereof may not be effective for time series with...
conference paper 2023
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Money, Rohan (author), Krishnan, Joshin (author), Beferull-Lozano, Baltasar (author), Isufi, E. (author)
An online topology estimation algorithm for nonlinear structural equation models (SEM) is proposed in this paper, addressing the nonlinearity and the non-stationarity of real-world systems. The nonlinearity is modeled using kernel formulations, and the curse of dimensionality associated with the kernels is mitigated using random feature...
journal article 2023
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Habib, B. (author), Isufi, E. (author), Breda, Ward van (author), Jongepier, Arjen (author), Cremer, Jochen (author)
Implementing accurate Distribution System State Estimation (DSSE) faces several challenges, among which the lack of observability and the high density of the distribution system. While data-driven alternatives based on Machine Learning models could be a choice, they suffer in DSSE because of the lack of labeled data. In fact, measurements in...
journal article 2023
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Sabbaqi, M. (author), Isufi, E. (author)
Devising and analysing learning models for spatiotemporal network data is of importance for tasks including forecasting, anomaly detection, and multi-agent coordination, among others. Graph Convolutional Neural Networks (GCNNs) are an established approach to learn from time-invariant network data. The graph convolution operation offers a...
journal article 2023
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Ben Saad, Leila (author), Beferull-Lozano, Baltasar (author), Isufi, E. (author)
Distributed graph filters have recently found applications in wireless sensor networks (WSNs) to solve distributed tasks such as reaching consensus, signal denoising, and reconstruction. However, when implemented over WSNs, the graph filters should deal with network limited energy constraints as well as processing and communication...
journal article 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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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), 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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Yang, Maosheng (author), Isufi, E. (author)
Reconstructing simplicial signals, e.g., signals defined on nodes, edges, triangles, etc., of a network, from (partial) noisy observation is of interest in water/traffic flow estimation or currency exchange markets. Typically, this concerns solving a regularised problem w.r.t. the l2 norm of the divergence or the curl of the signal, i.e., the...
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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Das, B. (author), Isufi, E. (author)
Performing signal processing over graphs requires knowledge of the underlying fixed topology. However, graphs often grow in size with new nodes appearing over time, whose connectivity is typically unknown; hence, making more challenging the downstream tasks in applications like cold start recommendation. We address such a challenge for signal...
conference paper 2022
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Isufi, E. (author), Yang, Maosheng (author)
This paper proposes convolutional filtering for data whose structure can be modeled by a simplicial complex (SC). SCs are mathematical tools that not only capture pairwise relationships as graphs but account also for higher-order network structures. These filters are built by following the shift-and-sum principle of the convolution operation and...
conference paper 2022
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Money, Rohan (author), Krishnan, Joshin (author), Beferull-Lozano, Baltasar (author), Isufi, E. (author)
An online algorithm for missing data imputation for networks with signals defined on the edges is presented. Leveraging the prior knowledge intrinsic to real-world networks, we propose a bi-level optimization scheme that exploits the causal dependencies and the flow conservation, respectively via <italic>(i)</italic> a sparse line...
journal article 2022
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