5 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…
Learning Optimal Graph Filters for Clustering of Attributed Graphs
Meiby Ortiz-Bouza, Selin Aviyente
Many real-world systems can be represented as graphs where the different entities in the system are presented by nodes and their interactions by edges. An important task in studyin…