1 citations · 1 across the 2 of their papers we have counts for
12 papers
Signed Graph Learning: Algorithms and Theory
Abdullah Karaaslanli, Bisakh Banerjee, Tapabrata Maiti +1
Real-world data is often represented through the relationships between data samples, forming a graph structure. In many applications, it is necessary to learn this graph structure…
Hypergraph Overlapping Community Detection for Brain Networks
Duc Vu, Selin Aviyente
Functional magnetic resonance imaging (fMRI) has been commonly used to construct functional connectivity networks (FCNs) of the human brain. TFCNs are primarily limited to quantify…
Learning Graph Filters for Structure-Function Coupling based Hub Node Identification
Meiby Ortiz-Bouza, Duc Vu, Abdullah Karaaslanli +1
Over the past two decades, tools from network science have been leveraged to characterize the organization of both structural and functional networks of the brain. One such measure…
Discriminative community detection for multiplex networks
Meiby Ortiz-Bouza, Selin Aviyente
Multiplex networks have emerged as a promising approach for modeling complex systems, where each layer represents a different mode of interaction among entities of the same type. A…
Community Detection in Multi-frequency EEG Networks
Abdullah Karaaslanli, Meiby Ortiz-Bouza, Tamanna T. K. Munia +1
Objective: In recent years, the functional connectivity of the human brain has been studied with graph theoretical tools. One such approach is community detection which is fundamen…
Coupled Support Tensor Machine Classification for Multimodal Neuroimaging Data
Li Peide, Seyyid Emre Sofuoglu, Tapabrata Maiti +1
Multimodal data arise in various applications where information about the same phenomenon is acquired from multiple sensors and across different imaging modalities. Learning from m…