8 citations · 25 across the 13 of their papers we have counts for
15 papers
UncertainGen: Uncertainty-Aware Representations of DNA Sequences for Metagenomic Binning
Abdulkadir Celikkanat, Andres R. Masegosa, Mads Albertsen +1
Metagenomic binning aims to cluster DNA fragments from mixed microbial samples into their respective genomes, a critical step for downstream analyses of microbial communities. Exis…
Revisiting K-mer Profile for Effective and Scalable Genome Representation Learning
Abdulkadir Celikkanat, Andres R. Masegosa, Thomas D. Nielsen
Obtaining effective representations of DNA sequences is crucial for genome analysis. Metagenomic binning, for instance, relies on genome representations to cluster complex mixtures…
Time to Cite: Modeling Citation Networks using the Dynamic Impact Single-Event Embedding Model
Nikolaos Nakis, Abdulkadir Celikkanat, Louis Boucherie +2
Understanding the structure and dynamics of scientific research, i.e., the science of science (SciSci), has become an important area of research in order to address imminent questi…
Continuous-time Graph Representation with Sequential Survival Process
Abdulkadir Celikkanat, Nikolaos Nakis, Morten Mørup
Over the past two decades, there has been a tremendous increase in the growth of representation learning methods for graphs, with numerous applications across various fields, inclu…
A Hybrid Membership Latent Distance Model for Unsigned and Signed Integer Weighted Networks
Nikolaos Nakis, Abdulkadir Çelikkanat, Morten Mørup
Graph representation learning (GRL) has become a prominent tool for furthering the understanding of complex networks providing tools for network embedding, link prediction, and nod…
Characterizing Polarization in Social Networks using the Signed Relational Latent Distance Model
Nikolaos Nakis, Abdulkadir Çelikkanat, Louis Boucherie +5
Graph representation learning has become a prominent tool for the characterization and understanding of the structure of networks in general and social networks in particular. Typi…
Piecewise-Velocity Model for Learning Continuous-time Dynamic Node Representations
Abdulkadir Çelikkanat, Nikolaos Nakis, Morten Mørup
Networks have become indispensable and ubiquitous structures in many fields to model the interactions among different entities, such as friendship in social networks or protein int…
HM-LDM: A Hybrid-Membership Latent Distance Model
Nikolaos Nakis, Abdulkadir Çelikkanat, Morten Mørup
A central aim of modeling complex networks is to accurately embed networks in order to detect structures and predict link and node properties. The latent space models (LSM) have be…
A Hierarchical Block Distance Model for Ultra Low-Dimensional Graph Representations
Nikolaos Nakis, Abdulkadir Çelikkanat, Sune Lehmann Jørgensen +1
Graph Representation Learning (GRL) has become central for characterizing structures of complex networks and performing tasks such as link prediction, node classification, network…
Topic-aware latent models for representation learning on networks
Abdulkadir Çelikkanat, Fragkiskos D. Malliaros
Network representation learning (NRL) methods have received significant attention over the last years thanks to their success in several graph analysis problems, including node cla…
Multiple Kernel Representation Learning on Networks
Abdulkadir Celikkanat, Yanning Shen, Fragkiskos D. Malliaros
Learning representations of nodes in a low dimensional space is a crucial task with numerous interesting applications in network analysis, including link prediction, node classific…
NodeSig: Binary Node Embeddings via Random Walk Diffusion
Abdulkadir Çelikkanat, Fragkiskos D. Malliaros, Apostolos N. Papadopoulos
Graph Representation Learning (GRL) has become a key paradigm in network analysis, with a plethora of interdisciplinary applications. As the scale of networks increases, most of th…
Exponential Family Graph Embeddings
Abdulkadir Çelikkanat, Fragkiskos D. Malliaros
Representing networks in a low dimensional latent space is a crucial task with many interesting applications in graph learning problems, such as link prediction and node classifica…
Kernel Node Embeddings
Abdulkadir Çelikkanat, Fragkiskos D. Malliaros
Learning representations of nodes in a low dimensional space is a crucial task with many interesting applications in network analysis, including link prediction and node classifica…
TNE: A Latent Model for Representation Learning on Networks
Abdulkadir Çelikkanat, Fragkiskos D. Malliaros
Network representation learning (NRL) methods aim to map each vertex into a low dimensional space by preserving the local and global structure of a given network, and in recent yea…