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 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 …

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 …

Since many real world networks are evolving over time, such as social networks and user-
item networks, there are increasing research efforts on dynamic network embedding in …

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

Conventional static knowledge graphs model entities in relational data as nodes, connected
by edges of specific relation types. However, information and knowledge evolve …

Graph representation learning has been a very active research area in recent years. The
goal of graph representation learning is to generate graph representation vectors that …

Temporal networks have been widely used to model real-world complex systems such as
financial systems and e-commerce systems. In a temporal network, the joint neighborhood of …

Hypergraph Convolutional Network (HCN) has be-come a proper choice for capturing high-
order relationships. Existing HCN methods are tailored for static hypergraphs, which are …