Search Results for author: Bingbing Xu

Found 17 papers, 8 papers with code

TEA: Test-time Energy Adaptation

1 code implementation24 Nov 2023 Yige Yuan, Bingbing Xu, Liang Hou, Fei Sun, HuaWei Shen, Xueqi Cheng

To address this, we propose a novel energy-based perspective, enhancing the model's perception of target data distributions without requiring access to training data or processes.


Bridged-GNN: Knowledge Bridge Learning for Effective Knowledge Transfer

no code implementations18 Aug 2023 Wendong Bi, Xueqi Cheng, Bingbing Xu, Xiaoqian Sun, Li Xu, HuaWei Shen

Transfer learning has been a feasible way to transfer knowledge from high-quality external data of source domains to limited data of target domains, which follows a domain-level knowledge transfer to learn a shared posterior distribution.

Retrieval Transfer Learning

When Spatio-Temporal Meet Wavelets: Disentangled Traffic Forecasting via Efficient Spectral Graph Attention Networks

1 code implementation IEEE 39th International Conference on Data Engineering (ICDE) 2023 Yuchen Fang, Yanjun Qin, Haiyong Luo, Fang Zhao, Bingbing Xu, Liang Zeng, Chenxing Wang

To capture these intricate dependencies, spatio-temporal networks, such as recurrent neural networks with graph convolution networks, graph convolution networks with temporal convolution networks, and temporal attention networks with full graph attention networks, are applied.

Graph Attention Traffic Prediction

PDE+: Enhancing Generalization via PDE with Adaptive Distributional Diffusion

1 code implementation25 May 2023 Yige Yuan, Bingbing Xu, Bo Lin, Liang Hou, Fei Sun, HuaWei Shen, Xueqi Cheng

The generalization of neural networks is a central challenge in machine learning, especially concerning the performance under distributions that differ from training ones.

Data Augmentation

IDEA: Invariant Causal Defense for Graph Adversarial Robustness

no code implementations25 May 2023 Shuchang Tao, Qi Cao, HuaWei Shen, Yunfan Wu, Bingbing Xu, Xueqi Cheng

Through modeling and analyzing the causal relationships in graph adversarial attacks, we design two invariance objectives to learn the causal features.

Adversarial Robustness

Predicting the Silent Majority on Graphs: Knowledge Transferable Graph Neural Network

1 code implementation2 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.

Representation Learning

Company-as-Tribe: Company Financial Risk Assessment on Tribe-Style Graph with Hierarchical Graph Neural Networks

1 code implementation31 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)).

Contrastive Learning Graph Learning

Towards Generalizable Graph Contrastive Learning: An Information Theory Perspective

no code implementations20 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.

Contrastive Learning Data Augmentation +1

Hierarchical Estimation for Effective and Efficient Sampling Graph Neural Network

no code implementations16 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.

Time Series Time Series Analysis

Spatio-Temporal meets Wavelet: Disentangled Traffic Flow Forecasting via Efficient Spectral Graph Attention Network

no code implementations6 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.

Graph Attention Time Series Analysis

Label-Consistency based Graph Neural Networks for Semi-supervised Node Classification

no code implementations27 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.

Classification General Classification +2

Graph Convolutional Networks using Heat Kernel for Semi-supervised Learning

1 code implementation27 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.

ANAE: Learning Node Context Representation for Attributed Network Embedding

no code implementations20 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.

Attribute General Classification +3

Graph Wavelet Neural Network

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.

General Classification

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