5 citations · 18 across the 11 of their papers we have counts for
16 papers
Learn to Code Sustainably: An Empirical Study on LLM-based Green Code Generation
Tina Vartziotis, Ippolyti Dellatolas, George Dasoulas +5
The increasing use of information technology has led to a significant share of energy consumption and carbon emissions from data centers. These contributions are expected to rise w…
Learn2Extend: Extending sequences by retaining their statistical properties with mixture models
Dimitris Vartziotis, George Dasoulas, Florian Pausinger
This paper addresses the challenge of extending general finite sequences of real numbers within a subinterval of the real line, maintaining their inherent statistical properties by…
GNNDelete: A General Strategy for Unlearning in Graph Neural Networks
Jiali Cheng, George Dasoulas, Huan He +2
Graph unlearning, which involves deleting graph elements such as nodes, node labels, and relationships from a trained graph neural network (GNN) model, is crucial for real-world ap…
New Frontiers in Graph Autoencoders: Joint Community Detection and Link Prediction
Guillaume Salha-Galvan, Johannes F. Lutzeyer, George Dasoulas +2
Graph autoencoders (GAE) and variational graph autoencoders (VGAE) emerged as powerful methods for link prediction (LP). Their performances are less impressive on community detecti…
Multimodal learning with graphs
Yasha Ektefaie, George Dasoulas, Ayush Noori +2
Artificial intelligence for graphs has achieved remarkable success in modeling complex systems, ranging from dynamic networks in biology to interacting particle systems in physics.…
Graph Ordering Attention Networks
Michail Chatzianastasis, Johannes F. Lutzeyer, George Dasoulas +1
Graph Neural Networks (GNNs) have been successfully used in many problems involving graph-structured data, achieving state-of-the-art performance. GNNs typically employ a message-p…
Modularity-Aware Graph Autoencoders for Joint Community Detection and Link Prediction
Guillaume Salha-Galvan, Johannes F. Lutzeyer, George Dasoulas +2
Graph autoencoders (GAE) and variational graph autoencoders (VGAE) emerged as powerful methods for link prediction. Their performances are less impressive on community detection pr…
Permute Me Softly: Learning Soft Permutations for Graph Representations
Giannis Nikolentzos, George Dasoulas, Michalis Vazirgiannis
Graph neural networks (GNNs) have recently emerged as a dominant paradigm for machine learning with graphs. Research on GNNs has mainly focused on the family of message passing neu…
Graph-based Neural Architecture Search with Operation Embeddings
Michail Chatzianastasis, George Dasoulas, Georgios Siolas +1
Neural Architecture Search (NAS) has recently gained increased attention, as a class of approaches that automatically searches in an input space of network architectures. A crucial…
Lipschitz Normalization for Self-Attention Layers with Application to Graph Neural Networks
George Dasoulas, Kevin Scaman, Aladin Virmaux
Attention based neural networks are state of the art in a large range of applications. However, their performance tends to degrade when the number of layers increases. In this work…
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…
Learning Parametrised Graph Shift Operators
George Dasoulas, Johannes Lutzeyer, Michalis Vazirgiannis
In many domains data is currently represented as graphs and therefore, the graph representation of this data becomes increasingly important in machine learning. Network data is, im…
Hcore-Init: Neural Network Initialization based on Graph Degeneracy
Stratis Limnios, George Dasoulas, Dimitrios M. Thilikos +1
Neural networks are the pinnacle of Artificial Intelligence, as in recent years we witnessed many novel architectures, learning and optimization techniques for deep learning. Capit…
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…
Coloring graph neural networks for node disambiguation
George Dasoulas, Ludovic Dos Santos, Kevin Scaman +1
In this paper, we show that a simple coloring scheme can improve, both theoretically and empirically, the expressive power of Message Passing Neural Networks(MPNNs). More specifica…
k-hop Graph Neural Networks
Giannis Nikolentzos, George Dasoulas, Michalis Vazirgiannis
Graph neural networks (GNNs) have emerged recently as a powerful architecture for learning node and graph representations. Standard GNNs have the same expressive power as the Weisf…