21 citations · 24 across the 9 of their papers we have counts for
10 papers
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…
Prot2Text-V2: Protein Function Prediction with Multimodal Contrastive Alignment
Xiao Fei, Michail Chatzianastasis, Sarah Almeida Carneiro +3
Predicting protein function from sequence is a central challenge in computational biology. While existing methods rely heavily on structured ontologies or similarity-based techniqu…
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…
Graph Linearization Methods for Reasoning on Graphs with Large Language Models
Christos Xypolopoulos, Guokan Shang, Xiao Fei +6
Large language models have evolved to process multiple modalities beyond text, such as images and audio, which motivates us to explore how to effectively leverage them for graph re…
Metrics to Detect Small-Scale and Large-Scale Citation Orchestration
Iakovos Evdaimon, John P. A. Ioannidis, Giannis Nikolentzos +3
Citation counts and related metrics have pervasive uses and misuses in academia and research appraisal, serving as scholarly influence and recognition measures. Hence, comprehendin…
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…
Geometric Self-Supervised Pretraining on 3D Protein Structures using Subgraphs
Michail Chatzianastasis, Yang Zhang, George Dasoulas +1
Protein representation learning aims to learn informative protein embeddings capable of addressing crucial biological questions, such as protein function prediction. Although seque…
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…