no code implementations • 2 Feb 2023 • Wendong Bi, Bingbing Xu, Xiaoqian Sun, Li Xu, HuaWei Shen, Xueqi Cheng
To combat the above challenges, we propose Knowledge Transferable Graph Neural Network (KT-GNN), which models distribution shifts during message passing and representation learning by transferring knowledge from vocal nodes to silent nodes.
1 code implementation • 31 Jan 2023 • Wendong Bi, Bingbing Xu, Xiaoqian Sun, Zidong Wang, HuaWei Shen, Xueqi Cheng
However, most nodes in the tribe-style graph lack attributes, making it difficult to directly adopt existing graph learning methods (e. g., Graph Neural Networks(GNNs)).
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 • 16 Nov 2022 • Yang Li, Bingbing Xu, Qi Cao, Yige Yuan, HuaWei Shen
On account that previous studies either lacks variance analysis or only focus on a particular sampling paradigm, we firstly propose an unified node sampling variance analysis framework and analyze the core challenge "circular dependency" for deriving the minimum variance sampler, i. e., sampling probability depends on node embeddings while node embeddings can not be calculated until sampling is finished.
no code implementations • 19 Oct 2022 • Kaike Zhang, Qi Cao, Gaolin Fang, Bingbing Xu, Hongjian Zou, HuaWei Shen, Xueqi Cheng
Unsupervised representation learning for dynamic graphs has attracted a lot of research attention in recent years.
no code implementations • 22 Mar 2022 • Zhaohui Wang, Qi Cao, HuaWei Shen, Bingbing Xu, Xueqi Cheng
The expressive power of message passing GNNs is upper-bounded by Weisfeiler-Lehman (WL) test.
no code implementations • 6 Dec 2021 • Yuchen Fang, Yanjun Qin, Haiyong Luo, Fang Zhao, Bingbing Xu, Chenxing Wang, Liang Zeng
Traffic forecasting is crucial for public safety and resource optimization, yet is very challenging due to three aspects: i) current existing works mostly exploit intricate temporal patterns (e. g., the short-term thunderstorm and long-term daily trends) within a single method, which fail to accurately capture spatio-temporal dependencies under different schemas; ii) the under-exploration of the graph positional encoding limit the extraction of spatial information in the commonly used full graph attention network; iii) the quadratic complexity of the full graph attention introduces heavy computational needs.
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 • 27 Jul 2020 • Bingbing Xu, Jun-Jie Huang, Liang Hou, Hua-Wei Shen, Jinhua Gao, Xue-Qi Cheng
Graph neural networks (GNNs) achieve remarkable success in graph-based semi-supervised node classification, leveraging the information from neighboring nodes to improve the representation learning of target node.
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 • ICLR 2019 • Bingbing Xu, Hua-Wei Shen, Qi Cao, Yunqi Qiu, Xue-Qi Cheng
We present graph wavelet neural network (GWNN), a novel graph convolutional neural network (CNN), leveraging graph wavelet transform to address the shortcomings of previous spectral graph CNN methods that depend on graph Fourier transform.
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