Recent studies have shown that Binary Graph Neural Networks (GNNs) are
p...
Applications that fuse machine learning and simulation can benefit from ...
Molecular property calculations are the bedrock of chemical physics.
Hig...
The demonstrated success of transfer learning has popularized approaches...
Hyperbolic neural networks have recently gained significant attention du...
Design of new drug compounds with target properties is a key area of res...
Intermolecular and long-range interactions are central to phenomena as
d...
Representation learning methods for heterogeneous networks produce a
low...
Representation learning methods that transform encoded data (e.g., diagn...
Lateral movement attacks are a serious threat to enterprise security. In...
Many real world networks are considered temporal networks, in which the
...
Link prediction, or predicting the likelihood of a link in a knowledge g...
Many modern datasets can be represented as graphs and hence spectral
dec...
Networks-of-networks (NoN) is a graph-theoretic model of interdependent
...