4 citations · 4 across the 2 of their papers we have counts for
7 papers
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…
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…
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…