351 citations · 577 across the 16 of their papers we have counts for
7 papers · 1 filter
A Survey on Deep Graph Generation: Methods and Applications
Yanqiao Zhu, Yuanqi Du, Yinkai Wang +4
Graphs are ubiquitous in encoding relational information of real-world objects in many domains. Graph generation, whose purpose is to generate new graphs from a distribution simila…
Are we really making much progress? Revisiting, benchmarking, and refining heterogeneous graph neural networks
Qingsong Lv, Ming Ding, Qiang Liu +7
Heterogeneous graph neural networks (HGNNs) have been blossoming in recent years, but the unique data processing and evaluation setups used by each work obstruct a full understandi…
An Empirical Study of Graph Contrastive Learning
Yanqiao Zhu, Yichen Xu, Qiang Liu +1
Graph Contrastive Learning (GCL) establishes a new paradigm for learning graph representations without human annotations. Although remarkable progress has been witnessed recently,…
Structure-Aware Hard Negative Mining for Heterogeneous Graph Contrastive Learning
Yanqiao Zhu, Yichen Xu, Hejie Cui +3
Recently, heterogeneous Graph Neural Networks (GNNs) have become a de facto model for analyzing HGs, while most of them rely on a relative large number of labeled data. In this wor…
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…
A Survey on Graph Structure Learning: Progress and Opportunities
Yanqiao Zhu, Weizhi Xu, Jinghao Zhang +5
Graphs are widely used to describe real-world objects and their interactions. Graph Neural Networks (GNNs) as a de facto model for analyzing graphstructured data, are highly sensit…
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,…