68 citations · 70 across the 8 of their papers we have counts for
8 papers
Towards the Explainability of Temporal Graph Networks via Memory Backtracking and Topological Attribution
Yazheng Liu, Xi Zhang, Sihong Xie +1
Temporal graphs are ubiquitous in real-world applications and Temporal Graph Networks (TGNs) have achieved superior predictive accuracy. Understanding which historical events drive…
SAM-Sode: Towards Faithful Explanations for Tiny Bacteria Detection
Wanying Tan, Shuo Yan, Dazhi Huang +7
Interpretability in object detection provides crucial confidence support for clinical auxiliary diagnosis. However, in tiny bacteria detection, traditional explanation methods ofte…
SCPRM: A Schema-aware Cumulative Process Reward Model for Knowledge Graph Question Answering
Jiujiu Chen, Yazheng Liu, Sihong Xie +1
Large language models excel at complex reasoning, yet evaluating their intermediate steps remains challenging. Although process reward models provide step-wise supervision, they of…
Attribution-Guided Continual Learning for Large Language Models
Yazheng Liu, Yuxuan Wan, Rui Xu +3
Large language models (LLMs) often suffer from catastrophic forgetting in continual learning: after learning new tasks sequentially, they perform worse on earlier tasks. Existing m…
A Differential Geometric View and Explainability of GNN on Evolving Graphs
Yazheng Liu, Xi Zhang, Sihong Xie
Graphs are ubiquitous in social networks and biochemistry, where Graph Neural Networks (GNN) are the state-of-the-art models for prediction. Graphs can be evolving and it is vital…
Inconsistent Matters: A Knowledge-guided Dual-consistency Network for Multi-modal Rumor Detection
Mengzhu Sun, Xi Zhang, Jianqiang Ma +3
Rumor spreaders are increasingly utilizing multimedia content to attract the attention and trust of news consumers. Though quite a few rumor detection models have exploited the mul…
Multi-objective Explanations of GNN Predictions
Yifei Liu, Chao Chen, Yazheng Liu +2
Graph Neural Network (GNN) has achieved state-of-the-art performance in various high-stake prediction tasks, but multiple layers of aggregations on graphs with irregular structures…
Explaining GNN over Evolving Graphs using Information Flow
Yazheng Liu, Xi Zhang, Sihong Xie
Graphs are ubiquitous in many applications, such as social networks, knowledge graphs, smart grids, etc.. Graph neural networks (GNN) are the current state-of-the-art for these app…