78 citations · 230 across the 36 of their papers we have counts for
7 papers · 1 filter
TEA: Test-time Energy Adaptation
Yige Yuan, Bingbing Xu, Liang Hou +3
Test-time adaptation (TTA) aims to improve model generalizability when test data diverges from training distribution, offering the distinct advantage of not requiring access to tra…
Significant-attributed Community Search in Heterogeneous Information Networks
Yanghao Liu, Fangda Guo, Bingbing Xu +3
Community search is a personalized community discovery problem aimed at finding densely-connected subgraphs containing the query vertex. In particular, the search for communities w…
Bridged-GNN: Knowledge Bridge Learning for Effective Knowledge Transfer
Wendong Bi, Xueqi Cheng, Bingbing Xu +3
The data-hungry problem, characterized by insufficiency and low-quality of data, poses obstacles for deep learning models. Transfer learning has been a feasible way to transfer kno…
PDE+: Enhancing Generalization via PDE with Adaptive Distributional Diffusion
Yige Yuan, Bingbing Xu, Bo Lin +4
The generalization of neural networks is a central challenge in machine learning, especially concerning the performance under distributions that differ from training ones. Current…
IDEA: Invariant Defense for Graph Adversarial Robustness
Shuchang Tao, Qi Cao, Huawei Shen +3
Despite the success of graph neural networks (GNNs), their vulnerability to adversarial attacks poses tremendous challenges for practical applications. Existing defense methods suf…
Predicting the Silent Majority on Graphs: Knowledge Transferable Graph Neural Network
Wendong Bi, Bingbing Xu, Xiaoqian Sun +3
Graphs consisting of vocal nodes ("the vocal minority") and silent nodes ("the silent majority"), namely VS-Graph, are ubiquitous in the real world. The vocal nodes tend to have ab…
Company-as-Tribe: Company Financial Risk Assessment on Tribe-Style Graph with Hierarchical Graph Neural Networks
Wendong Bi, Bingbing Xu, Xiaoqian Sun +3
Company financial risk is ubiquitous and early risk assessment for listed companies can avoid considerable losses. Traditional methods mainly focus on the financial statements of c…