Stationary time-vertex signal processing von Loukas, Andreas, Perraudin, Nathanaël

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RosettaSurf-A surface-centric computational design approach von Scheck, Andreas, Rosset, Stéphane, Defferrard, Michaël, Loukas, Andreas, Bonet, Jaume, Vandergheynst, Pierre, Correia, Bruno E

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Autoregressive Moving Average Graph Filtering von Isufi, Elvin, Loukas, Andreas, Simonetto, Andrea, Leus, Geert

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A Time-Vertex Signal Processing Framework: Scalable Processing and Meaningful Representations for Time-Series on Graphs von Grassi, Francesco, Loukas, Andreas, Perraudin, Nathanael, Ricaud, Benjamin

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Distributed Autoregressive Moving Average Graph Filters von Loukas, Andreas, Simonetto, Andrea, Leus, Geert

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Filtering Random Graph Processes Over Random Time-Varying Graphs von Isufi, Elvin, Loukas, Andreas, Simonetto, Andrea, Leus, Geert

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Forecasting Time Series With VARMA Recursions on Graphs von Isufi, Elvin, Loukas, Andreas, Perraudin, Nathanael, Leus, Geert

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rDAN: Toward robust demand-aware network designs von Avin, Chen, Hercules, Alexandr, Loukas, Andreas, Schmid, Stefan

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Dynamic Balanced Graph Partitioning von Avin, Chen, Bienkowski, Marcin, Loukas, Andreas, Pacut, Maciej, Schmid, Stefan

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Exploring “dark-matter” protein folds using deep learning von Harteveld, Zander, Van Hall-Beauvais, Alexandra, Morozova, Irina, Southern, Joshua, Goverde, Casper, Georgeon, Sandrine, Rosset, Stéphane, Defferrard, Michëal, Loukas, Andreas, Vandergheynst, Pierre, Bronstein, Michael M., Correia, Bruno E.

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How hard is to distinguish graphs with graph neural networks? von Loukas, Andreas

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What graph neural networks cannot learn: depth vs width von Loukas, Andreas

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Graph reduction with spectral and cut guarantees von Loukas, Andreas

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Erdos Goes Neural: an Unsupervised Learning Framework for Combinatorial Optimization on Graphs von Karalias, Nikolaos, Loukas, Andreas

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Infusing Lattice Symmetry Priors in Attention Mechanisms for Sample-Efficient Abstract Geometric Reasoning von Atzeni, Mattia, Sachan, Mrinmaya, Loukas, Andreas

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Online Balanced Repartitioning von Avin, Chen, Loukas, Andreas, Pacut, Maciej, Schmid, Stefan

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How close are the eigenvectors and eigenvalues of the sample and actual covariance matrices? von Loukas, Andreas

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Generalizing to any diverse distribution: uniformity, gentle finetuning and rebalancing von Loukas, Andreas, Martinkus, Karolis, Wagstaff, Ed, Cho, Kyunghyun

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How hard is to distinguish graphs with graph neural networks? von Loukas, Andreas

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Implicitly Guided Design with PropEn: Match your Data to Follow the Gradient von Tagasovska, Nataša, Gligorijević, Vladimir, Cho, Kyunghyun, Loukas, Andreas

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