3 citations · 6 across the 17 of their papers we have counts for
17 papers
Rank Is Not Capacity: Spectral Occupancy for Latent Graph Models
Nikolaos Nakis, Panagiotis Promponas, Konstantinos Tsirkas +4
Graph representation learning has become a standard approach for analyzing networked data, with latent embeddings widely used for link prediction, community detection, and related…
Aitchison Embeddings for Learning Compositional Graph Representations
Nikolaos Nakis, Chrysoula Kosma, Panagiotis Promponas +2
Representation learning is central to graph machine learning, powering tasks such as link prediction and node classification. However, most graph embeddings are hard to interpret,…
Benchmark for Assessing Olfactory Perception of Large Language Models
Eftychia Makri, Nikolaos Nakis, Laura Sisson +4
Here we introduce the Olfactory Perception (OP) benchmark, designed to assess the capability of large language models (LLMs) to reason about smell. The benchmark contains 1,010 que…
Archetypal Graph Generative Models: Explainable and Identifiable Communities via Anchor-Dominant Convex Hulls
Nikolaos Nakis, Chrysoula Kosma, Panagiotis Promponas +2
Representation learning has been essential for graph machine learning tasks such as link prediction, community detection, and network visualization. Despite recent advances in achi…
Deep description of static and dynamic network ties in Honduran villages
Marios Papamichalis, Nikolaos Nakis, Nicholas A. Christakis
We examine static and dynamic social network structure in 176 villages within the Copan Department of Honduras across two data waves (2016, 2019), using detailed data on multiplex…
Machine Learning for Static and Single-Event Dynamic Complex Network Analysis
Nikolaos Nakis
The primary objective of this thesis is to develop novel algorithmic approaches for Graph Representation Learning of static and single-event dynamic networks. In such a direction,…
Modeling roles and trade-offs in multiplex networks
Nikolaos Nakis, Sune Lehmann, Nicholas A. Christakis +1
A multiplex social network captures multiple types of social relations among the same set of people, with each layer representing a distinct type of relationship. Understanding the…
The Signed Two-Space Proximity Model for Learning Representations in Protein-Protein Interaction Networks
Nikolaos Nakis, Chrysoula Kosma, Anastasia Brativnyk +3
Accurately predicting complex protein-protein interactions (PPIs) is crucial for decoding biological processes, from cellular functioning to disease mechanisms. However, experiment…
How Low Can You Go? Searching for the Intrinsic Dimensionality of Complex Networks using Metric Node Embeddings
Nikolaos Nakis, Niels Raunkjær Holm, Andreas Lyhne Fiehn +1
Low-dimensional embeddings are essential for machine learning tasks involving graphs, such as node classification, link prediction, community detection, network visualization, and…
Signed Graph Autoencoder for Explainable and Polarization-Aware Network Embeddings
Nikolaos Nakis, Chrysoula Kosma, Giannis Nikolentzos +3
Autoencoders based on Graph Neural Networks (GNNs) have garnered significant attention in recent years for their ability to extract informative latent representations, characterizi…
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…