Multi-modal knowledge graphs (MKGs) include not only the relation triplets, but also related
multi-modal auxiliary data (ie, texts and images), which enhance the diversity of knowledge …

Temporal knowledge graphs (TKGs) model the temporal evolution of events and have
recently attracted increasing attention. Since TKGs are intrinsically incomplete, it is …

As a common technology in social network, clustering has attracted lots of research interest
due to its high performance, and many clustering methods have been presented. The most …

Given a heterogeneous information network (HIN) H, a head node h, a meta-path P, and a
tail node t, the meta-path prediction aims at predicting whether h can be linked to t by an …

The knowledge graph (KG) is an efficient form of knowledge organization and expression,
providing prior knowledge support for various downstream tasks, and has received …

Inductive link prediction (ILP) is to predict links for unseen entities in emerging knowledge
graphs (KGs), considering the evolving nature of KGs. A more challenging scenario is that …

Sparse temporal knowledge graph completion is a particular task in temporal knowledge
graph completion, which aims to use the sparse knowledge in the sparse temporal …

The proliferation of uncertain spatiotemporal data has led to an increasing demand for fuzzy
spatiotemporal knowledge modeling in various applications. However, performing multihop …

Network embedding plays a critical role in many applications. Node classification, link
prediction, and network visualization are examples of such applications. Attributed network …

The Point-of-Interest (POI) recommendation system in location-based social networks is
pivotal, offering versatile applications. Personalized recommendations hinge on pre …