1.5k citations · 6k across the 75 of their papers we have counts for
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Deep Contrastive Multiview Network Embedding
Mengqi Zhang, Yanqiao Zhu, Qiang Liu +2
Multiview network embedding aims at projecting nodes in the network to low-dimensional vectors, while preserving their multiple relations and attribute information. Contrastive lea…
DyGCN: Dynamic Graph Embedding with Graph Convolutional Network
Zeyu Cui, Zekun Li, Shu Wu +4
Graph embedding, aiming to learn low-dimensional representations (aka. embeddings) of nodes, has received significant attention recently. Recent years have witnessed a surge of eff…
Graph Classification by Mixture of Diverse Experts
Fenyu Hu, Liping Wang, Shu Wu +2
Graph classification is a challenging research problem in many applications across a broad range of domains. In these applications, it is very common that class distribution is imb…
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…
GraphAIR: Graph Representation Learning with Neighborhood Aggregation and Interaction
Fenyu Hu, Yanqiao Zhu, Shu Wu +3
Graph representation learning is of paramount importance for a variety of graph analytical tasks, ranging from node classification to community detection. Recently, graph convoluti…