As the study of graph neural networks becomes more intensive and
compreh...
Heterogeneous graph neural networks (GNNs) have been successful in handl...
Many real-world data can be modeled as heterogeneous graphs that contain...
Based on the significant improvement of model robustness by AT (Adversar...
Recent studies have shown that deep neural networks-based recommender sy...
Session-based recommendation is a challenging problem in the real-world
...
The industry and academia have proposed many distributed graph processin...
Deformation component analysis is a fundamental problem in geometry
proc...
Feature engineering, a crucial step of machine learning, aims to extract...
Finding or monitoring subgraph instances that are isomorphic to a given
...
Bayesian optimization is a broadly applied methodology to optimize the
e...