90 citations · 194 across the 29 of their papers we have counts for
19 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…
On the Lipschitz Continuity of Set Aggregation Functions and Neural Networks for Sets
Giannis Nikolentzos, Konstantinos Skianis
The Lipschitz constant of a neural network is connected to several important properties of the network such as its robustness and generalization. It is thus useful in many settings…
Graph Reasoning with Large Language Models via Pseudo-code Prompting
Konstantinos Skianis, Giannis Nikolentzos, Michalis Vazirgiannis
Large language models (LLMs) have recently achieved remarkable success in various reasoning tasks in the field of natural language processing. This success of LLMs has also motivat…
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…
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…
Time-Parameterized Convolutional Neural Networks for Irregularly Sampled Time Series
Chrysoula Kosma, Giannis Nikolentzos, Michalis Vazirgiannis
Irregularly sampled multivariate time series are ubiquitous in several application domains, leading to sparse, not fully-observed and non-aligned observations across different vari…
Supervised Attention Using Homophily in Graph Neural Networks
Michail Chatzianastasis, Giannis Nikolentzos, Michalis Vazirgiannis
Graph neural networks have become the standard approach for dealing with learning problems on graphs. Among the different variants of graph neural networks, graph attention network…
Path Neural Networks: Expressive and Accurate Graph Neural Networks
Gaspard Michel, Giannis Nikolentzos, Johannes Lutzeyer +1
Graph neural networks (GNNs) have recently become the standard approach for learning with graph-structured data. Prior work has shed light into their potential, but also their limi…
What Do GNNs Actually Learn? Towards Understanding their Representations
Giannis Nikolentzos, Michail Chatzianastasis, Michalis Vazirgiannis
In recent years, graph neural networks (GNNs) have achieved great success in the field of graph representation learning. Although prior work has shed light on the expressiveness of…
Weisfeiler and Leman go Hyperbolic: Learning Distance Preserving Node Representations
Giannis Nikolentzos, Michail Chatzianastasis, Michalis Vazirgiannis
In recent years, graph neural networks (GNNs) have emerged as a promising tool for solving machine learning problems on graphs. Most GNNs are members of the family of message passi…
Time Series Forecasting Models Copy the Past: How to Mitigate
Chrysoula Kosma, Giannis Nikolentzos, Nancy Xu +1
Time series forecasting is at the core of important application domains posing significant challenges to machine learning algorithms. Recently neural network architectures have bee…
A Highly Effective Low-Rank Compression of Deep Neural Networks with Modified Beam-Search and Modified Stable Rank
Moonjung Eo, Suhyun Kang, Wonjong Rhee
Compression has emerged as one of the essential deep learning research topics, especially for the edge devices that have limited computation power and storage capacity. Among the m…
Ego-based Entropy Measures for Structural Representations on Graphs
George Dasoulas, Giannis Nikolentzos, Kevin Scaman +2
Machine learning on graph-structured data has attracted high research interest due to the emergence of Graph Neural Networks (GNNs). Most of the proposed GNNs are based on the node…
EvoNet: A Neural Network for Predicting the Evolution of Dynamic Graphs
Changmin Wu, Giannis Nikolentzos, Michalis Vazirgiannis
Neural networks for structured data like graphs have been studied extensively in recent years. To date, the bulk of research activity has focused mainly on static graphs. However,…
Ego-based Entropy Measures for Structural Representations
George Dasoulas, Giannis Nikolentzos, Kevin Scaman +2
In complex networks, nodes that share similar structural characteristics often exhibit similar roles (e.g type of users in a social network or the hierarchical position of employee…
Rep the Set: Neural Networks for Learning Set Representations
Konstantinos Skianis, Giannis Nikolentzos, Stratis Limnios +1
In several domains, data objects can be decomposed into sets of simpler objects. It is then natural to represent each object as the set of its components or parts. Many conventiona…
Kernel Graph Convolutional Neural Networks
Giannis Nikolentzos, Polykarpos Meladianos, Antoine Jean-Pierre Tixier +2
Graph kernels have been successfully applied to many graph classification problems. Typically, a kernel is first designed, and then an SVM classifier is trained based on the featur…