375 citations · 2.2k across the 69 of their papers we have counts for
6 papers · 2 filters
Learning to Propagate for Graph Meta-Learning
Lu Liu, Tianyi Zhou, Guodong Long +2
Meta-learning extracts common knowledge from learning different tasks and uses it for unseen tasks. It can significantly improve tasks that suffer from insufficient training data,…
Attributed Graph Clustering: A Deep Attentional Embedding Approach
Chun Wang, Shirui Pan, Ruiqi Hu +3
Graph clustering is a fundamental task which discovers communities or groups in networks. Recent studies have mostly focused on developing deep learning approaches to learn a compa…
Prototype Propagation Networks (PPN) for Weakly-supervised Few-shot Learning on Category Graph
Lu Liu, Tianyi Zhou, Guodong Long +3
A variety of machine learning applications expect to achieve rapid learning from a limited number of labeled data. However, the success of most current models is the result of heav…
Graph WaveNet for Deep Spatial-Temporal Graph Modeling
Zonghan Wu, Shirui Pan, Guodong Long +2
Spatial-temporal graph modeling is an important task to analyze the spatial relations and temporal trends of components in a system. Existing approaches mostly capture the spatial…
DAGCN: Dual Attention Graph Convolutional Networks
Fengwen Chen, Shirui Pan, Jing Jiang +2
Graph convolutional networks (GCNs) have recently become one of the most powerful tools for graph analytics tasks in numerous applications, ranging from social networks and natural…
Learning Graph Embedding with Adversarial Training Methods
Shirui Pan, Ruiqi Hu, Sai-fu Fung +3
Graph embedding aims to transfer a graph into vectors to facilitate subsequent graph analytics tasks like link prediction and graph clustering. Most approaches on graph embedding f…