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

Twitter bot detection has become an increasingly important and challenging task to combat
online misinformation, facilitate social content moderation, and safeguard the integrity of …

Few-shot node classification aims at classifying nodes with limited labeled nodes as
references. Recent few-shot node classification methods typically learn from classes with …

Abstract Graph Contrastive Learning (GCL) has recently drawn much research interest for
learning generalizable node representations in a self-supervised manner. In general, the …

Few-shot node classification, which aims to predict labels for nodes on graphs with only
limited labeled nodes as references, is of great significance in real-world graph mining …

Graph self-supervised learning has attracted plenty of attention in recent years. However,
most existing methods are designed for homogeneous graphs yet not tailored for bipartite …

Fairness in graph neural networks has been actively studied recently. However, existing
works often do not explicitly consider the role of message passing in introducing or …

Session-based recommendation (SBR) aims at recommending items given the behavior
sequences of anonymous users in a short-term session. Many recent SBR methods …

Graph neural networks (GNNs) have become core building blocks behind a myriad of graph
learning tasks. The vast majority of the existing GNNs are built upon, either implicitly or …

Alzheimer's disease (AD) is a chronic and common form of dementia that mainly affects
elderly individuals. The disease is dangerous because it causes damage to brain cells and …