Designing new molecules is essential for drug discovery and material sci...
We propose DyGFormer, a new Transformer-based architecture for dynamic g...
Learning the underlying distribution of molecular graphs and generating
...
Graph generative models have broad applications in biology, chemistry an...
Traffic demand forecasting by deep neural networks has attracted widespr...
Graph Neural Networks (GNNs) have been widely applied in the semi-superv...
Given a sequence of sets, where each set is associated with a timestamp ...
Representation learning on heterogeneous graphs aims to obtain meaningfu...
Heterogeneous graphs are pervasive in practical scenarios, where each gr...
With the prevalence of Internet of Things (IoT) applications, IoT device...
Given a sequence of sets, where each set contains an arbitrary number of...
Social dilemmas exist in various fields and give rise to the so-called
f...
Though voting-based consensus algorithms in Blockchain outperform proof-...
In this paper, we present a novel approach to machine reading comprehens...
We present a simple yet effective approach for linking entities in queri...
Much work has been done on feature selection. Existing methods are based...
In order to control the process of data mining and focus on the things o...
Because the data being mined in the temporal database will evolve with t...