activity
20192026
most citedMeta-GNN: On Few-shot Node Classification in Graph Meta-learning

35 citations · 47 across the 6 of their papers we have counts for

collaborators

6 papers

cs.LG2026

Why Self-Inconsistency Arises in GNN Explanations and How to Exploit It

Wenxin Tai, Yaqian Liu, Ting Zhong +1

Recent work has observed that explanations produced by Self-Interpretable Graph Neural Networks (SI-GNNs) can be self-inconsistent: when the model is reapplied to its own explanato…

cs.IR2025★ 1 cited

Analyzing and Mitigating Repetitions in Trip Recommendation

Wenzheng Shu, Kangqi Xu, Wenxin Tai +3

Trip recommendation has emerged as a highly sought-after service over the past decade. Although current studies significantly understand human intention consistency, they struggle…

cs.CV2025

Retrieval-Augmented Dynamic Prompt Tuning for Incomplete Multimodal Learning

Jian Lang, Zhangtao Cheng, Ting Zhong +1

Multimodal learning with incomplete modality is practical and challenging. Recently, researchers have focused on enhancing the robustness of pre-trained MultiModal Transformers (MM…

cs.SI2020★ 10 cited

A Heterogeneous Dynamical Graph Neural Networks Approach to Quantify Scientific Impact

Fan Zhou, Xovee Xu, Ce Li +3

Quantifying and predicting the long-term impact of scientific writings or individual scholars has important implications for many policy decisions, such as funding proposal evaluat…

cs.LG2020★ 1 cited

Overcoming Catastrophic Forgetting in Graph Neural Networks with Experience Replay

Fan Zhou, Chengtai Cao

Graph Neural Networks (GNNs) have recently received significant research attention due to their superior performance on a variety of graph-related learning tasks. Most of the curre…

cs.LG2019★ 35 cited

Meta-GNN: On Few-shot Node Classification in Graph Meta-learning

Fan Zhou, Chengtai Cao, Kunpeng Zhang +3

Meta-learning has received a tremendous recent attention as a possible approach for mimicking human intelligence, i.e., acquiring new knowledge and skills with little or even no de…