6 papers
SpaTeoGL: Spatiotemporal Graph Learning for Interpretable Seizure Onset Zone Analysis from Intracranial EEG
Elham Rostami, Aref Einizade, Taous-Meriem Laleg-Kirati
Accurate localization of the seizure onset zone (SOZ) from intracranial EEG (iEEG) is essential for epilepsy surgery but is challenged by complex spatiotemporal seizure dynamics. W…
Spatiotemporal Imputation with Graph-Informed Flow Matching
Zepeng Zhang, Aref Einizade, Jhony H. Giraldo +1
Missing data is a common challenge in spatiotemporal systems, arising in applications such as air quality monitoring and urban traffic management. Traditional machine learning appr…
Scaling Higher-Order Graph Learning with Maximal Clique Complexes
Antoine Vialle, Aref Einizade, Fragkiskos D. Malliaros +1
Graph neural networks (GNNs) are limited to modeling pairwise interactions, while higher-order models based on cell complexes achieve greater expressivity but often suffer from poo…
Continuous Simplicial Neural Networks
Aref Einizade, Dorina Thanou, Fragkiskos D. Malliaros +1
Simplicial complexes provide a powerful framework for modeling higher-order interactions in structured data, making them particularly suitable for applications such as trajectory p…
Second-Order Tensorial Partial Differential Equations on Graphs
Aref Einizade, Fragkiskos D. Malliaros, Jhony H. Giraldo
Processing data on multiple interacting graphs is crucial for many applications, but existing approaches rely mostly on discrete filtering or first-order continuous models, dampeni…
Subgraph Gaussian Embedding Contrast for Self-Supervised Graph Representation Learning
Shifeng Xie, Aref Einizade, Jhony H. Giraldo
Graph Representation Learning (GRL) is a fundamental task in machine learning, aiming to encode high-dimensional graph-structured data into low-dimensional vectors. Self-Supervised…