no code implementations • 20 Nov 2022 • Yige Yuan, Bingbing Xu, HuaWei Shen, Qi Cao, Keting Cen, Wen Zheng, Xueqi Cheng
Guided by the bound, we design a GCL framework named InfoAdv with enhanced generalization ability, which jointly optimizes the generalization metric and InfoMax to strike the right balance between pretext task fitting and the generalization ability on downstream tasks.
no code implementations • 1 Jan 2021 • Xu Bingbing, HuaWei Shen, Qi Cao, YuanHao Liu, Keting Cen, Xueqi Cheng
For a target node, diverse sampling offers it diverse neighborhoods, i. e., rooted sub-graphs, and the representation of target node is finally obtained via aggregating the representation of diverse neighborhoods obtained using any GNN model.
1 code implementation • 27 Jul 2020 • Bingbing Xu, Hua-Wei Shen, Qi Cao, Keting Cen, Xue-Qi Cheng
Graph convolutional networks gain remarkable success in semi-supervised learning on graph structured data.
no code implementations • 20 Jun 2019 • Keting Cen, Hua-Wei Shen, Jinhua Gao, Qi Cao, Bingbing Xu, Xue-Qi Cheng
In this paper, we address attributed network embedding from a novel perspective, i. e., learning node context representation for each node via modeling its attributed local subgraph.
1 code implementation • CIKM 2017 • Qi Cao, HuaWei Shen, Keting Cen, Wentao Ouyang, Xueqi Cheng
In this paper, we propose DeepHawkes to combat the defects of existing methods, leveraging end-to-end deep learning to make an analogy to interpretable factors of Hawkes process — a widely-used generative process to model information cascade.