28 citations · 39 across the 8 of their papers we have counts for
9 papers
A Graph Neural Architecture Search Approach for Identifying Bots in Social Media
Georgios Tzoumanekas, Michail Chatzianastasis, Loukas Ilias +3
Social media platforms, including X, Facebook, and Instagram, host millions of daily users, giving rise to bots-automated programs disseminating misinformation and ideologies with…
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…
Prot2Text: Multimodal Protein's Function Generation with GNNs and Transformers
Hadi Abdine, Michail Chatzianastasis, Costas Bouyioukos +1
In recent years, significant progress has been made in the field of protein function prediction with the development of various machine-learning approaches. However, most existing…
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…
Neural Architecture Search with Multimodal Fusion Methods for Diagnosing Dementia
Michail Chatzianastasis, Loukas Ilias, Dimitris Askounis +1
Alzheimer's dementia (AD) affects memory, thinking, and language, deteriorating person's life. An early diagnosis is very important as it enables the person to receive medical help…
Explainable Multilayer Graph Neural Network for Cancer Gene Prediction
Michail Chatzianastasis, Michalis Vazirgiannis, Zijun Zhang
The identification of cancer genes is a critical yet challenging problem in cancer genomics research. Existing computational methods, including deep graph neural networks, fail to…
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…
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…
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…