Graph analytics can lead to better quantitative understanding and control of complex
networks, but traditional methods suffer from the high computational cost and excessive …

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

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 …

Inductive representation learning on temporal graphs is an important step toward salable
machine learning on real-world dynamic networks. The evolving nature of temporal dynamic …

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

Modeling sequential interactions between users and items/products is crucial in domains
such as e-commerce, social networking, and education. Representation learning presents …

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 …

Representation Learning over graph structured data has received significant attention
recently due to its ubiquitous applicability. However, most advancements have been made …

Graphs arise naturally in many real-world applications including social networks,
recommender systems, ontologies, biology, and computational finance. Traditionally …