Many real-world graph learning tasks require handling dynamic graphs whe...
Researches on analyzing graphs with Graph Neural Networks (GNNs) have be...
In the intersection of molecular science and deep learning, tasks like
v...
Spatial-temporal forecasting has attracted tremendous attention in a wid...
Well-designed molecular representations (fingerprints) are vital to comb...
Graph convolutional networks (GCNs) have received considerable research
...
Graph neural networks (GNNs) have achieved tremendous success in graph
m...
Learning low-dimensional representations on graphs has proved to be effe...
Information networks are ubiquitous and are ideal for modeling relationa...
Graph neural networks (GNNs) have achieved strong performance in various...
Graph Convolutional Networks (GCNs) achieved tremendous success by
effec...
The interactions of users and items in recommender system could be natur...
It is not until recently that graph neural networks (GNNs) are adopted t...
Time series modeling has attracted extensive research efforts; however,
...
Recent works reveal that network embedding techniques enable many machin...
Graph Convolutional Networks (GCNs) have proved to be a most powerful
ar...
Many successful methods have been proposed for learning low dimensional
...