Liu, C., Leus, G. and Isufi, E. (2023). Unrolling of Simplicial ElasticNet for Edge Flow Signal Reconstruction. IEEE Open Journal on Signal Processing. [https://doi.org/10.1109/OJSP.2023.3339376]
Sabbaqi, M. and Isufi, E. (2023). Graph-Time Convolutional Neural Networks: Architecture and Theoretical Analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence. [https://ieeexplore.ieee.org/abstract/document/10239277]
Habib, B., Isufi, E., Van Breda, W., Jongepier, A. and Cremer, J.L. (2023). Deep Statistical Solver for Distribution System State Estimation. IEEE Transactions on Power Systems. [https://arxiv.org/pdf/2301.01835.pdf]
Gao, Z. and Isufi, E. (2023). Learning Stochastic Graph Neural Networks with Constrained Variance. IEEE Transactions on Signal Processing. [https://ieeexplore.ieee.org/document/10042031]
Das, B., Hanjalic, A. and Isufi, E. (2022). Task-Aware Connectivity Learning for Incoming Nodes on Growing Graphs. IEEE Transactions on Signal and Information Processing over Networks. [https://ieeexplore.ieee.org/document/9900466]
Gao, Z., Isufi, E. and Ribeiro, A. (2022). Stochastic Graph Neural Networks. IEEE Transactions on Signal Processing. [https://arxiv.org/abs/2006.02684]
Vrachimis, S.G., Eliades, D. G., Taormina, R., Kapelan, Z., Ostfeld, F.ASCE, A., Liu, S., Aff.M.ASCE, Kyriakou, M.; Pavlou, P.; Qiu, M. and Polycarpou, M.M. (2022). Battle of the leakage detection and isolation methods. Journal of Water Resources Planning and Management. [https://ascelibrary.org/doi/10.1061/%28ASCE%29WR.1943-5452.0001601]
Garzón, A., Kapelan, Z., Langeveld, J. and Taormina, R. (2022). Machine Learning-Based Surrogate Modeling for Urban Water Networks: Review and Future Research Directions. Water Resources Research. [https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021WR031808]
Yang, M., Isufi, E., Schaub, M.T. and Leus, G. (2022). Simplicial Convolutional Filters. IEEE Transactions on Signal Processing. [https://arxiv.org/abs/2201.11720]
Isufi, E., Gama, F., D. Shuman, I. and Segarra, S. (2022). Graph Filters for Signal Processing and Machine Learning on Graphs. IEEE Transactions on Signal Processing. [https://arxiv.org/pdf/2211.08854.pdf]
Money, R., Krishnan, J., Beferull-Lozano, B. and Isufi, E. (2022). Online Missing Data Imputation of Edge Flows. IEEE Signal Processing Letters. [https://ieeexplore.ieee.org/document/9947283]
Yang, M., Isufi, E., M. Schaub, T. and Leus, G. (2022). Simplicial Convolutional Filters. IEEE Transactions on Signal Processing. [https://arxiv.org/abs/2201.11720]
Das, B., Hanjalic, A. and Isufi, E. (2022). Task-Aware Connectivity Learning for Incoming Nodes on Growing Graphs. IEEE Transactions on Signal and Information Processing over Networks. [https://ieeexplore.ieee.org/document/9900466]
Natali, A., Isufi, E., Coutino, M. and Leus, G. (2022). Learning Time-Varying Graphs from Online Data. IEEE Open Journal on Signal Processing. [https://arxiv.org/abs/2110.11017]
Di Nardo, A., Boccelli, D. L., Herrera, M., Creaco, E., Cominola, A., Sitzenfrei, R., & Taormina, R. (2021). Smart Urban Water Networks: Solutions, Trends and Challenges. MDPI. [https://www.mdpi.com/2073-4441/13/4/501]
Gama, F., Isufi, E., Leus, G. and Ribeiro, A. (2021). Graphs, convolutionsand neural networks: From graph filters to graph neural networks. IEEE Signal Processing Magazine. [https://arxiv.org/abs/2003.03777]
Ben Saad, L., Beferull-Lozano, B. and Isufi, E. (2021). Quantization Analysis and Robust Design for Distributed Graph Filters. IEEE Transactions on Signal Processing. [https://ieeexplore.ieee.org/document/9665348]
Gao, Z., Isufi, E. and Ribeiro, A. (2021). Stochastic Graph Neural Networks. IEEE Transactions on Signal Processing. [https://arxiv.org/pdf/2006.02684.pdf]
Yang, M., Coutino, M., Leus, G. and Isufi, E. (2021). Node-Adaptive Regularization for Graph Signal Reconstruction. IEEE Open Journal of Signal Processing. [https://ieeexplore.ieee.org/document/9346013]
Isufi, E., Pocchiari, M. and Hanjalic, A. (2021). Accuracy-Diversity Trade-off in Recommender Systems via Graph Convolutions. Elsevier Information Processing and Management; Special Issue on Advances in Graph Representation Learning for Large-scale Multimedia Analytics. [https://www.sciencedirect.com/science/article/pii/S0306457320309511]
Gama, F., Isufi, E., Leus, G. and Ribeiro, A. (2020). Graphs, Convolutions and Neural Networks: From Graph Filters to Graph Neural Networks. IEEE Signal Processing Magazine; Special Issue on Graph Signal Processing: Foundations and Emerging Directions. [https://ieeexplore.ieee.org/document/9244191]
Coutino, M., Isufi, E., Maehara, T. and Leus, G. (2020). State-Space Network Topology Identification from Partial Observations. IEEE Transactions on Signal and Information Processing over Networks; Special Issue on Network Topology Identification. [https://ieeexplore.ieee.org/document/9005190]
Money, R., Krishnan, J., Beferull-Lozano, B. and Isufi, E. (2024). Evolution Backcasting of Edge Flows from Partial Observations Using Simplicial Vector Autoregressive Models. IEEE International Conference on Acoustic Speech and Signal Processing, (ICASSP), South Korea. []
Möllers, A., Immer, A., Fortuin, V. and Isufi, E. (2024). Hodge-Aware Contrastive Learning. IEEE International Conference on Acoustic Speech and Signal Processing, (ICASSP), South Korea. [https://arxiv.org/abs/2309.07364]
Buciulea, A., Isufi, E., Leus, G. and Marques, A.G. (2024). Learning Graphs and Simplicial Complexes from Data. IEEE International Conference on Acoustic Speech and Signal Processing, (ICASSP), South Korea. []
Habib, B., Isufi, E. and Cremer, J.L. (2024). Weakly Supervised Graph Neural Network For State Estimation in Unobservable Distribution Systems. IEEE International Conference on Acoustic Speech and Signal Processing, (ICASSP), South Korea. []
Sabbaqi, M. and Isufi, E. (2024). Inferring Time Varying Signals over Uncertain Graphs. IEEE International Conference on Acoustic Speech and Signal Processing, (ICASSP), South Korea. []
Das, B. and Isufi, E. (2024). Tensor Graph Decomposition for Temporal Networks. IEEE International Conference on Acoustic Speech and Signal Processing, (ICASSP), South Korea. []
Yang, M. and Isufi, E. (2023). Convolutional Learning on Simplicial Complexes. International Conference on Machine Learning. [https://arxiv.org/pdf/2301.11163.pdf]
Möllers, A., Immer, A., Isufi, E. and Fortuin, V. (2023). Uncertainty in Graph Contrastive Learning with Bayesian Neural Networks. 5th Symposium on Advances in Approximate Bayesian Inference collocated with ICML. [https://openreview.net/forum?id=LH76pl-OUj]
Sabbaqi, M. and Isufi, E., (2023). Graph-Time Trend Filtering and Unrolling Network. EURASIP European Signal Processing Conference (EUSIPCO), Helsinki, Finland. [https://ieeexplore.ieee.org/document/10289885]
Bentivoglio, R., Isufi, E., S. Jonkman, N. and Taormina, R. (2023). On the Generalization of Hydraulic-Inspired Graph Neural Networks for Spatio-temporal Flood Simulations. European Geoscience Union (EGU) General Assembly. [https://meetingorganizer.copernicus.org/EGU23/session/46382]
Krishnan, J., Money, R., Beferull-Lozano, B. and Isufi, E. (2023). Simplicial Vector Autoregressive Model for Streaming Edge Flows. IEEE International Conference on Acoustic, Speech and Signal Processing, (ICASSP), Greece. [https://ieeexplore.ieee.org/document/10289885]
Das, B. and Isufi, E. (2023). Online Vector Autoregressive Models over Expanding Graphs. IEEE International Conference on Acoustic, Speech and Signal Processing, (ICASSP), Greece. [https://ieeexplore.ieee.org/abstract/document/10096508]
Yang, M., Das, B. and E.Isufi (2023). Online Edge Flow Prediction over Expanding Simplicial Complexes. IEEE International Conference on Acoustic, Speech and Signal Processing, (ICASSP), Greece. [https://ieeexplore.ieee.org/abstract/document/10096364]
Bentivoglio, R., Isufi, E., Nicolaas, S. and Taormina, R. (2023). Graph Neural Networks for Dike Breach Flood Mapping. 9th International Conference on Flood Management (ICFM9). []
Bentivoglio, R., Kerimov, B., Diaz, J.A.G., Isufi, E., Tscheikner-Grati, F., Steffelbauer, D.B. and Taormina, R. (2022). Assessing the Performance and Transferability of Graph Neural Network Metamodels for Water Distribution Systems. 2nd WDSA/CCWI Joint Conference Water Distribution System Analysis Computing and Control in Water Industry; Valencia, Spain. [https://iwaponline.com/jh/article/doi/10.2166/hydro.2023.031/98159/Assessing-the-performances-and-transferability-of]
Kerimov, B., Tscheikner-Gratl, F., Taormina, R. and Steffelbauer D. (2022). The Shape of Water Distribution Networks - Describing local structures of water networks via graphlet analysis. 2nd WDSA/CCWI Joint Conference Water Distribution System Analysis Computing and Control in Water Industry; Valencia, Spain. [https://doi.org/10.4995/WDSA-CCWI2022.2022.14784]
Das B. and Isufi, E. (2022). Graph Filtering Over Expanding Graphs. IEEE Data Science and Learning Workshop (DSLW), Singapore. [https://arxiv.org/abs/2203.08058]
Isufi, E. and Yang. M. (2022). Convolutional Filtering in Simplicial Complexes. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Singapore. [https://arxiv.org/abs/2201.12584v1]
Yang, M., Isufi, E. and Leus, G. (2022). Simplicial Convolutional Neural Networks. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Singapore. [https://arxiv.org/abs/2110.02585]
Das, B. and Isufi, E. (2022). Learning Expanding Graphs for Signal Interpolation. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Singapore. [https://arxiv.org/abs/2203.07966]
Das, B. and Isufi, E. (2022). Online Filtering over Expanding Graphs. IEEE Asilomar Conference on Signals, Systems and Computations, Pacific Grove, USA. [https://arxiv.org/abs/2301.06898v1]
Roca, A.S., A. G. Díaz, Isufi, E. and Taormina, R. (2022). EPANET Metamodels with Deep Unrolling of the Global Gradient Algorithm. WSDA / CCWI Joint Conference. []
Sabbaqi, M., Taormina, R., Hanjalic, A. and Isufi, E. (2022). Graph-Time Convolutional Autoencoders. Proceedings of the First Learning on Graphs Conference (Log 2022), PMLR 198, Virtual. [https://openreview.net/forum?id=2HqKwHaBwv]
Gao, Z. and Isufi, E. (2022). Learning Stable Graph Neural Networks via Spectral Regularization. IEEE Asilomar Conference on Signals, Systems and Computations, Pacific Grove, USA. [https://arxiv.org/abs/2211.06966]
Yang, M. and Isufi, E. (2022). Simplicial Trend Filtering. IEEE Asilomar Conference on Signals, Systems and Computations, Pacific Grove, USA. [https://ieeexplore.ieee.org/document/10051892]
Das, B. and Isufi, E. (2022). Online Filtering over Expanding Graphs. IEEE Asilomar Conference on Signals, Systems and Computations, Pacific Grove, USA. [https://ieeexplore.ieee.org/document/10052045]
He, Y., Coutino, M., Isufi, E., and Leus, G. (2022). Dynamic Bi-colored Graph Partitioning. EURASIP European Signal Processing Conference (EUSIPCO), Belgrade, Serbia. [https://ieeexplore.ieee.org/document/9909839]
Das, B. and Isufi, E. (2022). Graph Filtering Over Expanding Graphs. IEEE Data Science and Learning Workshop (DSLW), Singapore. [https://arxiv.org/abs/2203.08058]
Isufi, E. and Yang, M. (2022). Convolutional Filters for Simplicial Complexes. EURASIP European Signal Processing Conference (EUSIPCO), Belgrade, Serbia. [https://arxiv.org/abs/2201.12584]
Yang, M., Isufi, E. and Leus, G. (2022). Simplicial Convolutional Neural Networks. IEEE Data Science and Learning Workshop (DSLW), Singapore. [https://arxiv.org/abs/2110.02585]
Das, B. and Isufi, E. (2022). Learning Expanding Graphs for Signal Interpolation. IEEE Data Science and Learning Workshop (DSLW), Singapore. [https://arxiv.org/abs/2203.07966]
Mavritsakis, P., Ten Veldhuis, M.C., Schleiss, M. and Taormina, R. (2021). Dry-spell assessment through rainfall downscaling comparing deep-learning algorithms and conventional statistical frameworks in a data scarce region: The case of Northern Ghana. EGU General Assembly Conference Abstracts. [https://meetingorganizer.copernicus.org/EGU21/EGU21-8393.html]
Yang, M., Isufi, E., Schaub, T. and Leus, G. (2021). Finite Impulse Response Filters for Simplicial Complexes. 29th European Signal Processing Conference (EUSIPCO), Dublin Ireland. [https://arxiv.org/abs/2103.12587]
Ruiz, L., Gama, F., Ribeiro, A. and Isufi, E. (2021). Nonlinear State-Space Generalizations of Graph Convolutional Neural Networks. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Toronto, Ontario, Canada. [https://arxiv.org/abs/2010.14585]
Gao, Z., Isufi, E.and Ribeiro, A. (2021). Variance-Constrained Learning for Stochastic Graph Neural Networks. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Toronto, Ontario, Canada. [https://arxiv.org/abs/2201.12611]
Natali, A., Isufi, E., Coutino, M. and Leus, G. (2021). Online Graph Learning from Time-Varying Structural Equation Models. IEEE Asilomar Conference on Signals, Systems and Computations, Pacific Grove, USA. [https://ieeexplore.ieee.org/document/9723163]
Yang, M., Isufi, E., Schaub, M., T. and Leus, G. (2021). Finite Impulse Response Filters for Simplicial Complexes. EURASIP European Signal Processing Conference (EUSIPCO), Dublin, Ireland. [https://arxiv.org/abs/2103.12587]
Zhang, K., Coutino, M. and Isufi, E. (2021). Sampling Graph Signal with Sparse Dictionary Representation. EURASIP European Signal Processing Conference (EUSIPCO), Dublin, Ireland. [https://ieeexplore.ieee.org/document/9615918]
Isufi, E. and Mazzola, G. (2021). Graph-Time Convolutional Neural Networks. IEEE Data Science and Learning Workshop, Toronto, Ontario, Canada. [https://ieeexplore.ieee.org/document/9523412]
Garzon, A., Bentivoglio, R., Isufi, E., Kapelan, Z. and Taormina, R. (2021). Modeling Water Distribution Systems with Graph Neural Networks. European Geoscience Union (EGU) General Assembly. [https://ui.adsabs.harvard.edu/abs/2021EGUGA..23.9378G/abstract]
Ruiz, L., Gama, F., Ribeiro, A. and Isufi, E. (2021). Nonlinear State-Space Generalizations of Graph Convolutional Neural Networks. IEEE Data Science and Learning Workshop, Toronto, Ontario, Canada. [https://arxiv.org/abs/2010.14585]
Taormina, R., Ashrafi, M., Murillo, A. and Galelli, S. (2020). Deep Learning-based Surrogate Models for Water Distribution Systems. Deep Learning-based Surrogate Models for Water Distribution Systems - NASA/ADS (harvard.edu). [https://ui.adsabs.harvard.edu/abs/2020EGUGA..2222576T/abstract]
Natali, A., Isufi, E. and Leus, G. (2020). Forecasting multi-dimensional processes over graphs. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Barcelona Spain. [https://ieeexplore.ieee.org/document/9053522]
Iancu, B., Ruiz, L., Ribeiro, A. and Isufi, E. (2020). Graph-Adaptive Activation Functions for Graph Neural Networks. IEEE 30th International Workshop on Machine Learning for Signal Processing (MLSP), Espoo, Finland. [https://arxiv.org/abs/2009.06723]
Iancu, B., Ruiz, L., Ribeiro, A. and Isufi, E. (2020). Graph-Adaptive Activation Functions For Graph Neural Networks. IEEE International Workshop on Machine Learning for Signal Processing, Espoo, Finland. [https://ieeexplore.ieee.org/document/9231732]
Iancu, B. and Isufi, E. (2020). Towards Finite-Time Consensus with Graph Convolutional Neural Networks. EURASIP European Signal Processing Conference (EUSIPCO), Amsterdam, The Netherlands. [https://ieeexplore.ieee.org/abstract/document/9287610]
Das, B., Isufi, E. and Leus, G. (2020). Active Semi-supervised Learning for Diffusions on Graphs. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Barcelona, Spain. [https://ieeexplore.ieee.org/document/9054300?denied=]
Gao, Z., Isufi, E. and Ribeiro, A. (2020). Stochastic Graph Neural Networks. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Barcelona, Spain. [https://ieeexplore.ieee.org/abstract/document/9054424]