3 citations · 3 across the 8 of their papers we have counts for
10 papers
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…
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…
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…
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…
Higher-Order GNNs Meet Efficiency: Sparse Sobolev Graph Neural Networks
Jhony H. Giraldo, Aref Einizade, Andjela Todorovic +4
Graph Neural Networks (GNNs) have shown great promise in modeling relationships between nodes in a graph, but capturing higher-order relationships remains a challenge for large-sca…
Spatiotemporal Forecasting Meets Efficiency: Causal Graph Process Neural Networks
Aref Einizade, Fragkiskos D. Malliaros, Jhony H. Giraldo
Graph Neural Networks (GNNs) have advanced spatiotemporal forecasting by leveraging relational inductive biases among sensors (or any other measuring scheme) represented as nodes i…
Continuous Product Graph Neural Networks
Aref Einizade, Fragkiskos D. Malliaros, Jhony H. Giraldo
Processing multidomain data defined on multiple graphs holds significant potential in various practical applications in computer science. However, current methods are mostly limite…
Kernel-based Joint Multiple Graph Learning and Clustering of Graph Signals
Mohamad H. Alizade, Aref Einizade, Jhony H. Giraldo
Within the context of Graph Signal Processing (GSP), Graph Learning (GL) is concerned with the inference of the graph's underlying structure from nodal observations. However, real-…