3 citations · 5 across the 6 of their papers we have counts for
6 papers
CO-EVOLVE: Bidirectional Co-Evolution of Graph Structure and Semantics for Heterophilous Learning
Jinming Xing, Muhammad Shahzad
The integration of Large Language Models (LLMs) and Graph Neural Networks (GNNs) promises to unify semantic understanding with structural reasoning, yet existing methods typically…
FGATT: A Robust Framework for Wireless Data Imputation Using Fuzzy Graph Attention Networks and Transformer Encoders
Jinming Xing, Chang Xue, Dongwen Luo +1
Missing data is a pervasive challenge in wireless networks and many other domains, often compromising the performance of machine learning and deep learning models. To address this,…
Unifying Prediction and Explanation in Time-Series Transformers via Shapley-based Pretraining
Qisen Cheng, Jinming Xing, Chang Xue +1
In this paper, we propose ShapTST, a framework that enables time-series transformers to efficiently generate Shapley-value-based explanations alongside predictions in a single forw…
Multi-view Fuzzy Graph Attention Networks for Enhanced Graph Learning
Jinming Xing, Dongwen Luo, Qisen Cheng +2
Fuzzy Graph Attention Network (FGAT), which combines Fuzzy Rough Sets and Graph Attention Networks, has shown promise in tasks requiring robust graph-based learning. However, exist…
Comparative Analysis of Pooling Mechanisms in LLMs: A Sentiment Analysis Perspective
Jinming Xing, Dongwen Luo, Chang Xue +1
Large Language Models (LLMs) have revolutionized natural language processing (NLP) by delivering state-of-the-art performance across a variety of tasks. Among these, Transformer-ba…
Enhancing Link Prediction with Fuzzy Graph Attention Networks and Dynamic Negative Sampling
Jinming Xing, Ruilin Xing, Chang Xue +1
Link prediction is crucial for understanding complex networks but traditional Graph Neural Networks (GNNs) often rely on random negative sampling, leading to suboptimal performance…