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

Embedding static graphs in low-dimensional vector spaces plays a key role in network
analytics and inference, supporting applications like node classification, link prediction, and …

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 …

Graph Neural Networks (GNNs) have recently become increasingly popular due to their
ability to learn complex systems of relations or interactions arising in a broad spectrum of …

A d ynamically w eighted d irected n etwork (DWDN) is frequently encountered in various big
data-related applications like a terminal interaction pattern analysis system (TIPAS) …

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 …

Large knowledge graphs often grow to store temporal facts that model the dynamic relations
or interactions of entities along the timeline. Since such temporal knowledge graphs often …

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 …

Temporal networks serve as abstractions of many real-world dynamic systems. These
networks typically evolve according to certain laws, such as the law of triadic closure, which …