8 citations · 25 across the 13 of their papers we have counts for
8 papers · 1 filter
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…
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…
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…
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…
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…
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…