With the widespread use of information technologies, information networks are becoming
increasingly popular to capture complex relationships across various disciplines, such as …

This work considers the question of how convenient access to copious data impacts our
ability to learn causal effects and relations. In what ways is learning causality in the era of …

Graph Neural Networks (GNNs) have been successfully applied to many real-world static
graphs. However, the success of static graphs has not fully translated to dynamic graphs due …

Abstract The goal of Knowledge Tracing (KT) is to estimate how well students have
mastered a concept based on their historical learning of related exercises. The benefit of …

Representation learning in heterogeneous graphs aims to pursue a meaningful vector
representation for each node so as to facilitate downstream applications such as link …

Self-supervised learning (SSL) has been demonstrated to be effective in pre-training models
that can be generalized to various downstream tasks. Graph Autoencoder (GAE), an …

Learning node representations in graphs is important for many applications such as link
prediction, node classification, and community detection. Existing graph representation …

Graph representation learning resurges as a trending research subject owing to the
widespread use of deep learning for Euclidean data, which inspire various creative designs …

Deep learning on graphs has become one of the hottest topics in machine learning. The
book consists of four parts to best accommodate our readers with diverse backgrounds and …

Abstract Knowledge Tracing (KT) aims to trace the student's state of evolutionary mastery for
a particular knowledge or concept based on the student's historical learning interactions with …