396 citations · 441 across the 4 of their papers we have counts for
6 papers
Modeling User Behavior with Graph Convolution for Personalized Product Search
Fan Lu, Qimai Li, Bo Liu +7
User preference modeling is a vital yet challenging problem in personalized product search. In recent years, latent space based methods have achieved state-of-the-art performance b…
Clustering Uncertain Data via Representative Possible Worlds with Consistency Learning
Han Liu, Xianchao Zhang, Xiaotong Zhang +2
Clustering uncertain data is an essential task in data mining for the internet of things. Possible world based algorithms seem promising for clustering uncertain data. However, the…
Attributed Graph Clustering via Adaptive Graph Convolution
Xiaotong Zhang, Han Liu, Qimai Li +1
Attributed graph clustering is challenging as it requires joint modelling of graph structures and node attributes. Recent progress on graph convolutional networks has proved that g…
Label Efficient Semi-Supervised Learning via Graph Filtering
Qimai Li, Xiao-Ming Wu, Han Liu +2
Graph-based methods have been demonstrated as one of the most effective approaches for semi-supervised learning, as they can exploit the connectivity patterns between labeled and u…
Large Margin Few-Shot Learning
Yong Wang, Xiao-Ming Wu, Qimai Li +4
The key issue of few-shot learning is learning to generalize. This paper proposes a large margin principle to improve the generalization capacity of metric based methods for few-sh…
Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning
Qimai Li, Zhichao Han, Xiao-Ming Wu
Many interesting problems in machine learning are being revisited with new deep learning tools. For graph-based semisupervised learning, a recent important development is graph con…