1.1k citations · 1.8k across the 10 of their papers we have counts for
4 papers · 1 filter
When Contrastive Learning Meets Active Learning: A Novel Graph Active Learning Paradigm with Self-Supervision
Yanqiao Zhu, Weizhi Xu, Qiang Liu +1
This paper studies active learning (AL) on graphs, whose purpose is to discover the most informative nodes to maximize the performance of graph neural networks (GNNs). Previously,…
Graph Contrastive Learning with Adaptive Augmentation
Yanqiao Zhu, Yichen Xu, Feng Yu +3
Recently, contrastive learning (CL) has emerged as a successful method for unsupervised graph representation learning. Most graph CL methods first perform stochastic augmentation o…
CAGNN: Cluster-Aware Graph Neural Networks for Unsupervised Graph Representation Learning
Yanqiao Zhu, Yichen Xu, Feng Yu +2
Unsupervised graph representation learning aims to learn low-dimensional node embeddings without supervision while preserving graph topological structures and node attributive feat…
Deep Graph Contrastive Representation Learning
Yanqiao Zhu, Yichen Xu, Feng Yu +3
Graph representation learning nowadays becomes fundamental in analyzing graph-structured data. Inspired by recent success of contrastive methods, in this paper, we propose a novel…