1 citations · 1 across the 5 of their papers we have counts for
6 papers
Subspace Projection Methods for Fast Spectral Embeddings of Evolving Graphs
Mohammad Eini, Abdullah Karaaslanli, Vassilis Kalantzis +1
Several graph data mining, signal processing, and machine learning downstream tasks rely on information related to the eigenvectors of the associated adjacency or Laplacian matrix.…
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…
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…
Multiview Graph Learning with Consensus Graph
Abdullah Karaaslanli, Selin Aviyente
Graph topology inference, i.e., learning graphs from a given set of nodal observations, is a significant task in many application domains. Existing approaches are mostly limited to…
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…
Constrained Spectral Clustering for Dynamic Community Detection
Abdullah Karaaslanli, Selin Aviyente
Networks are useful representations of many systems with interacting entities, such as social, biological and physical systems. Characterizing the meso-scale organization, i.e. the…