Nowadays, research into personalization has been focusing on explainability and fairness.
Several approaches proposed in recent works are able to explain individual …

Graph Neural Networks (GNNs) have achieved great success in Knowledge Graph
Completion (KGC) by modelling how entities and relations interact in recent years. However …

In real world, a large number of networks are heterogeneous, containing different types of
semantics and connections. Existing studies typically only consider lower-order pairwise …

Drug repurposing has emerged as a effective and efficient strategy to identify new
treatments for a variety of diseases. One of the most effective approaches for discovering …

Graph representation learning methods, such as node embeddings, are powerful
approaches to map nodes into a latent vector space, allowing their use for various graph …

This survey paper presents a comprehensive and conceptual overview of anomaly detection
using dynamic graphs. We focus on existing graph-based anomaly detection (AD) …

The issue of drug safety has received increasing attention in modern society. Estimating the
frequency of drug side effects proves to be an effective approach to improving drug …

Abstract Heterogeneous Graph Contrastive Learning (HGCL) has attracted lots of attentions
because of eliminating the requirement of node labels. The encoders used in HGCL mainly …

Temporal knowledge graph reasoning (TKGR) has been considered as a crucial task for
modeling the evolving knowledge, aiming to infer the unknown connections between entities …

Knowledge graph completion (KGC) aims to alleviate the inherent incompleteness of
knowledge graphs (KGs), which is a critical task for various applications, such as …