364 citations · 415 across the 13 of their papers we have counts for
7 papers · 1 filter
KGNN: Distributed Framework for Graph Neural Knowledge Representation
Binbin Hu, Zhiyang Hu, Zhiqiang Zhang +2
Knowledge representation learning has been commonly adopted to incorporate knowledge graph (KG) into various online services. Although existing knowledge representation learning me…
Neural Graph Matching for Pre-training Graph Neural Networks
Yupeng Hou, Binbin Hu, Wayne Xin Zhao +3
Recently, graph neural networks (GNNs) have been shown powerful capacity at modeling structural data. However, when adapted to downstream tasks, it usually requires abundant task-s…
Confidence May Cheat: Self-Training on Graph Neural Networks under Distribution Shift
Hongrui Liu, Binbin Hu, Xiao Wang +3
Graph Convolutional Networks (GCNs) have recently attracted vast interest and achieved state-of-the-art performance on graphs, but its success could typically hinge on careful trai…
Conditional Attention Networks for Distilling Knowledge Graphs in Recommendation
Ke Tu, Peng Cui, Daixin Wang +4
Knowledge graph is generally incorporated into recommender systems to improve overall performance. Due to the generalization and scale of the knowledge graph, most knowledge relati…
MixSeq: Connecting Macroscopic Time Series Forecasting with Microscopic Time Series Data
Zhibo Zhu, Ziqi Liu, Ge Jin +4
Time series forecasting is widely used in business intelligence, e.g., forecast stock market price, sales, and help the analysis of data trend. Most time series of interest are mac…
Bandit Samplers for Training Graph Neural Networks
Ziqi Liu, Zhengwei Wu, Zhiqiang Zhang +4
Several sampling algorithms with variance reduction have been proposed for accelerating the training of Graph Convolution Networks (GCNs). However, due to the intractable computati…