5 papers · 1 filter
Does Graph Compression Preserve Signal Propagation?
Kawshik Banerjee, Khaled Mohammed Saifuddin
Graph compression reduces the computational cost of graph learning, but its effect on signal propagation remains largely underexplored. Existing work evaluates compression through…
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…
Tackling Oversmoothing in GNN via Graph Sparsification: A Truss-based Approach
Tanvir Hossain, Khaled Mohammed Saifuddin, Muhammad Ifte Khairul Islam +2
Graph Neural Network (GNN) achieves great success for node-level and graph-level tasks via encoding meaningful topological structures of networks in various domains, ranging from s…
HeTriNet: Heterogeneous Graph Triplet Attention Network for Drug-Target-Disease Interaction
Farhan Tanvir, Khaled Mohammed Saifuddin, Tanvir Hossain +2
Modeling the interactions between drugs, targets, and diseases is paramount in drug discovery and has significant implications for precision medicine and personalized treatments. C…
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…