With the explosive growth of artificial intelligence (AI) and big data, it has become vitally
important to organize and represent the enormous volume of knowledge appropriately. As …

Automatic knowledge graph construction aims at manufacturing structured human
knowledge. To this end, much effort has historically been spent extracting informative fact …

Graphs are widely used as a popular representation of the network structure of connected
data. Graph data can be found in a broad spectrum of application domains such as social …

Graph convolutional networks (GCNs)—which are effective in modeling graph structures—
have been increasingly popular in knowledge graph completion (KGC). GCN-based KGC …

Social science concerns issues on individuals, relationships, and the whole society. The
complexity of research topics in social science makes it the amalgamation of multiple …

Matching plays a vital role in the rational allocation of resources in many areas, ranging from
market operation to people's daily lives. In economics, the term matching theory is coined for …

The necessity for scholarly knowledge mining and management has grown significantly as
academic literature and its linkages to authors produce enormously. Information extraction …

Networks are a general language for describing complex systems of interacting entities. In
the real world, a network always contains massive nodes, edges and additional complex …

Graph Augmentation Learning (GAL) provides outstanding solutions for graph learning in
handling incomplete data, noise data, etc. Numerous GAL methods have been proposed for …

AI applications often use ML/DL (Machine Learning/Deep Learning) models to implement
specific AI tasks. As application developers usually are not AI experts, they often choose to …