most citedKGNN: Distributed Framework for Graph Neural Knowledge Representation

1 citations · 2 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG20221 cited

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…

cs.IR20221 cited

CORE: Simple and Effective Session-based Recommendation within Consistent Representation Space

Yupeng Hou, Binbin Hu, Zhiqiang Zhang +1

Session-based Recommendation (SBR) refers to the task of predicting the next item based on short-term user behaviors within an anonymous session. However, session embedding learned…

cs.LG2022

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…

cs.SI2022

An Effective Graph Learning based Approach for Temporal Link Prediction: The First Place of WSDM Cup 2022

Qian Zhao, Shuo Yang, Binbin Hu +5

Temporal link prediction, as one of the most crucial work in temporal graphs, has attracted lots of attention from the research area. The WSDM Cup 2022 seeks for solutions that pre…

cs.LG2022

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…