5 papers
Cross-attentive Cohesive Subgraph Embedding to Mitigate Oversquashing in GNNs
Tanvir Hossain, Muhammad Ifte Khairul Islam, Lilia Chebbah +2
Graph neural networks (GNNs) have achieved strong performance across various real-world domains. Nevertheless, they suffer from oversquashing, where long-range information is disto…
HyperGCL: Multi-Modal Graph Contrastive Learning via Learnable Hypergraph Views
Khaled Mohammed Saifuddin, Shihao Ji, Esra Akbas
Recent advancements in Graph Contrastive Learning (GCL) have demonstrated remarkable effectiveness in improving graph representations. However, relying on predefined augmentations…
Self-Clustering Graph Transformer Approach to Model Resting-State Functional Brain Activity
Bishal Thapaliya, Esra Akbas, Ram Sapkota +3
Resting-state functional magnetic resonance imaging (rs-fMRI) offers valuable insights into the human brain's functional organization and is a powerful tool for investigating the r…
Topology-guided Hypergraph Transformer Network: Unveiling Structural Insights for Improved Representation
Khaled Mohammed Saifuddin, Mehmet Emin Aktas, Esra Akbas
Hypergraphs, with their capacity to depict high-order relationships, have emerged as a significant extension of traditional graphs. Although Graph Neural Networks (GNNs) have remar…
Brain Networks and Intelligence: A Graph Neural Network Based Approach to Resting State fMRI Data
Bishal Thapaliya, Esra Akbas, Jiayu Chen +5
Resting-state functional magnetic resonance imaging (rsfMRI) is a powerful tool for investigating the relationship between brain function and cognitive processes as it allows for t…