5 papers
GraViti: Graph-Level Variational Autoencoders with Relaxed Permutation Invariance
Roman Bresson, Konstantinos Divriotis, Johannes F. Lutzeyer +2
We introduce GraViti, a transformer-based graph-level variational autoencoder that maps entire graphs to compact latent vectors. This design produces a true graph-level latent spac…
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…
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…
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…