3 papers
cs.CL2026
Where Detectors Fail: Closing the Tail-Domain Gap with Expert-Guided Mutual Distillation
Xuan Feng, Guihong Liu, Tianlong Gu +5
Multimodal fake news detectors often generalize poorly across domains because they learn to trust unreliable evidence: domain-specific shortcuts amplified by imbalanced data and se…
cs.LG2025
FairGSE: Fairness-Aware Graph Neural Network without High False Positive Rates
Zhenqiang Ye, Jinjie Lu, Tianlong Gu +2
Graph neural networks (GNNs) have emerged as the mainstream paradigm for graph representation learning due to their effective message aggregation. However, this advantage also ampl…
cs.LG2024
Towards Fair Graph Neural Networks via Graph Counterfactual without Sensitive Attributes
Xuemin Wang, Tianlong Gu, Xuguang Bao +1
Graph-structured data is ubiquitous in today's connected world, driving extensive research in graph analysis. Graph Neural Networks (GNNs) have shown great success in this field, l…