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