21 citations · 24 across the 9 of their papers we have counts for
6 papers · 1 filter
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…
Cell2Text: Multimodal LLM for Generating Single-Cell Descriptions from RNA-Seq Data
Oussama Kharouiche, Aris Markogiannakis, Xiao Fei +2
Single-cell RNA sequencing has transformed biology by enabling the measurement of gene expression at cellular resolution, providing information for cell types, states, and disease…
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…
KAGNNs: Kolmogorov-Arnold Networks meet Graph Learning
Roman Bresson, Giannis Nikolentzos, George Panagopoulos +3
In recent years, Graph Neural Networks (GNNs) have become the de facto tool for learning node and graph representations. Most GNNs typically consist of a sequence of neighborhood a…
Neural Graph Generator: Feature-Conditioned Graph Generation using Latent Diffusion Models
Iakovos Evdaimon, Giannis Nikolentzos, Christos Xypolopoulos +4
Graph generation has emerged as a crucial task in machine learning, with significant challenges in generating graphs that accurately reflect specific properties. Existing methods o…