The recovery of time-varying graph signals is a fundamental problem with...
Graph Neural Networks (GNNs) have been applied to many problems in compu...
Graph Neural Networks (GNNs) have been successfully applied in many
appl...
The discovery of drug-target interactions (DTIs) is a very promising are...
Network representation learning (NRL) methods have received significant
...
As the field of machine learning for combinatorial optimization advances...
Learning representations of nodes in a low dimensional space is a crucia...
Graph Neural Networks (GNNs) achieve significant performance for various...
Learning node representations is a crucial task with a plethora of
inter...
Representing networks in a low dimensional latent space is a crucial tas...
Learning representations of nodes in a low dimensional space is a crucia...
Although influence maximization has been studied extensively in the past...
Network representation learning (NRL) methods aim to map each vertex int...
Network embedding algorithms are able to learn latent feature representa...
Recent research has shown that graph degeneracy algorithms, which decomp...