3 citations · 6 across the 17 of their papers we have counts for
9 papers · 1 filter
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,…
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…
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,…
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…
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…