38 citations · 82 across the 16 of their papers we have counts for
21 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…
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…
DSAM: A Deep Learning Framework for Analyzing Temporal and Spatial Dynamics in Brain Networks
Bishal Thapaliya, Robyn Miller, Jiayu Chen +8
Resting-state functional magnetic resonance imaging (rs-fMRI) is a noninvasive technique pivotal for understanding human neural mechanisms of intricate cognitive processes. Most rs…
DyGCL: Dynamic Graph Contrastive Learning For Event Prediction
Muhammed Ifte Khairul Islam, Khaled Mohammed Saifuddin, Tanvir Hossain +1
Predicting events such as political protests, flu epidemics, and criminal activities is crucial to proactively taking necessary measures and implementing required responses to addr…
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…
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…
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…
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…
DDI Prediction via Heterogeneous Graph Attention Networks
Farhan Tanvir, Khaled Mohammed Saifuddin, Esra Akbas
Polypharmacy, defined as the use of multiple drugs together, is a standard treatment method, especially for severe and chronic diseases. However, using multiple drugs together may…
HyGNN: Drug-Drug Interaction Prediction via Hypergraph Neural Network
Khaled Mohammed Saifuddin, Briana Bumgardner, Farhan Tanvir +1
Drug-Drug Interactions (DDIs) may hamper the functionalities of drugs, and in the worst scenario, they may lead to adverse drug reactions (ADRs). Predicting all DDIs is a challengi…
Liars are more influential: Effect of Deception in Influence Maximization on Social Networks
Mehmet Emin Aktas, Esra Akbas, Ashley Hahn
Detecting influential users, called the influence maximization problem on social networks, is an important graph mining problem with many diverse applications such as information p…
Identifying critical higher-order interactions in complex networks
Mehmet Emin Aktas, Thu Nguyen, Sidra Jawaid +2
Information diffusion on networks is an important concept in network science observed in many situations such as information spreading and rumor controlling in social networks, dis…
Hypergraph Laplacians in Diffusion Framework
Mehmet Emin Aktas, Esra Akbas
Networks are important structures used to model complex systems where interactions take place. In a basic network model, entities are represented as nodes, and interaction and rela…
Graph Classification via Heat Diffusion on Simplicial Complexes
Mehmet Emin Aktas, Esra Akbas
In this paper, we study the graph classification problem in vertex-labeled graphs. Our main goal is to classify the graphs comparing their higher-order structures thanks to heat di…
Persistence Homology of Networks: Methods and Applications
Mehmet Emin Aktas, Esra Akbas, Ahmed El Fatmaoui
Information networks are becoming increasingly popular to capture complex relationships across various disciplines, such as social networks, citation networks, and biological netwo…
Network Embedding: on Compression and Learning
Esra Akbas, Mehmet Aktas
Recently, network embedding that encodes structural information of graphs into a vector space has become popular for network analysis. Although recent methods show promising perfor…
Computing the Braid Monodromy of Completely Reducible -gonal Curves
Mehmet Aktas, Esra Akbas
Braid monodromy is an important tool for computing invariants of curves and surfaces. In this paper, the \emph{rectangular braid diagram (RBD)} method is proposed to compute the br…