Heterogeneous social networks, which are characterized by diverse interaction types, have
resulted in new challenges for missing link prediction. Most deep learning models tend to …

Motivation The cellular system of a living organism is composed of interacting bio-molecules
that control cellular processes at multiple levels. Their correspondences are represented by …

Representing networks in a low dimensional latent space is a crucial task with many
interesting applications in graph learning problems, such as link prediction and node …

Accuracy and timeliness are indeed often conflicting goals in prediction tasks. Premature
predictions may yield a higher rate of false alarms, whereas delaying predictions to gather …

Network representation learning (NRL) methods have received significant attention over the
last years thanks to their success in several graph analysis problems, including node …

Attributed network representation learning aims at learning node embeddings by integrating
network structure and attribute information. It is a challenge to fully capture the microscopic …

Learning representations of nodes in a low dimensional space is a crucial task with
numerous interesting applications in network analysis, including link prediction, node …

Accuracy and timeliness are indeed often conflicting goals in prediction tasks. Premature
predictions may yield a higher rate of false alarms, whereas delaying predictions to gather …

Graph Representation Learning (GRL) has become central for characterizing structures of
complex networks and performing tasks such as link prediction, node classification, network …

Graph embedding, representing local and global neighbourhood information by numerical
vectors, is a crucial part of the mathematical modeling of a wide range of real-world systems …