Human knowledge provides a formal understanding of the world. Knowledge graphs that
represent structural relations between entities have become an increasingly popular …

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

Knowledge Graph (KG) reasoning that predicts missing facts for incomplete KGs has been
widely explored. However, reasoning over Temporal KG (TKG) that predicts facts in the …

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 …

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

Reasoning on temporal knowledge graphs (TKGR), aiming to infer missing events along the
timeline, has been widely studied to alleviate incompleteness issues in TKG, which is …

Dynamic networks are used in a wide range of fields, including social network analysis,
recommender systems and epidemiology. Representing complex networks as structures …

Temporal knowledge graph (TKG) reasoning is a crucial task that has gained increasing
research interest in recent years. Most existing methods focus on reasoning at past …

Modeling time-evolving knowledge graphs (KGs) has recently gained increasing interest.
Here, graph representation learning has become the dominant paradigm for link prediction …

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