6 citations · 7 across the 5 of their papers we have counts for
6 papers
SAC-GCL: Structure-Aware Compression for Graph Contrastive Learning
Muhammad Ifte khairul Islam, Tanvir Hossain, Esra Akbas
Graph Contrastive Learning (GCL) has achieved remarkable success in self-supervised representation learning by maximizing agreement between augmented views of a graph. Although aug…
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…
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…
Hypergraph Classification via Persistent Homology
Mehmet Emin Aktas, Thu Nguyen, Rakin Riza +2
Persistent homology is a mathematical tool used for studying the shape of data by extracting its topological features. It has gained popularity in network science due to its applic…
MPool: Motif-Based Graph Pooling
Muhammad Ifte Khairul Islam, Max Khanov, Esra Akbas
Graph Neural networks (GNNs) have recently become a powerful technique for many graph-related tasks including graph classification. Current GNN models apply different graph pooling…
Seq-HyGAN: Sequence Classification via Hypergraph Attention Network
Khaled Mohammed Saifuddin, Corey May, Farhan Tanvir +2
Sequence classification has a wide range of real-world applications in different domains, such as genome classification in health and anomaly detection in business. However, the la…