The adaptive processing of graph data is a long-standing research topic that has been lately
consolidated as a theme of major interest in the deep learning community. The snap …

Network representation learning (NRL) advances the conventional graph mining of social
networks, knowledge graphs, and complex biomedical and physics information networks …

AI is remarkably successful and outperforms human experts in certain tasks, even in
complex domains such as medicine. Humans on the other hand are experts at multi-modal …

While Graph Neural Networks (GNNs) have achieved remarkable results in a variety of
applications, recent studies exposed important shortcomings in their ability to capture the …

Federated learning has emerged as an important paradigm for training machine learning
models in different domains. For graph-level tasks such as graph classification, graphs can …

Graph convolutional networks (GCN) with one or two hidden layers have been widely used
in handling graph data that are prevalent in various disciplines. Many studies showed that …