3 citations · 3 across the 8 of their papers we have counts for
3 papers · 1 filter
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…