6 citations · 9 across the 7 of their papers we have counts for
7 papers
HeroFilter: Adaptive Spectral Graph Filter for Varying Heterophilic Relations
Shuaicheng Zhang, Haohui Wang, Junhong Lin +5
Graph heterophily, where connected nodes have different labels, has attracted significant interest recently. Most existing works adopt a simplified approach - using low-pass filter…
Scientific Hypothesis Generation and Validation: Methods, Datasets, and Future Directions
Adithya Kulkarni, Fatimah Alotaibi, Xinyue Zeng +7
Large Language Models (LLMs) are transforming scientific hypothesis generation and validation by enabling information synthesis, latent relationship discovery, and reasoning augmen…
When Heterophily Meets Heterogeneity: Challenges and a New Large-Scale Graph Benchmark
Junhong Lin, Xiaojie Guo, Shuaicheng Zhang +2
Graph mining has become crucial in fields such as social science, finance, and cybersecurity. Many large-scale real-world networks exhibit both heterogeneity, where multiple node a…
GPatcher: A Simple and Adaptive MLP Model for Alleviating Graph Heterophily
Shuaicheng Zhang, Haohui Wang, Si Zhang +1
While graph heterophily has been extensively studied in recent years, a fundamental research question largely remains nascent: How and to what extent will graph heterophily affect…
Personalized Federated Learning under Mixture of Distributions
Yue Wu, Shuaicheng Zhang, Wenchao Yu +5
The recent trend towards Personalized Federated Learning (PFL) has garnered significant attention as it allows for the training of models that are tailored to each client while mai…
Extracting Temporal Event Relation with Syntax-guided Graph Transformer
Shuaicheng Zhang, Lifu Huang, Qiang Ning
Extracting temporal relations (e.g., before, after, and simultaneous) among events is crucial to natural language understanding. One of the key challenges of this problem is that w…
Bilingual Dictionary-based Language Model Pretraining for Neural Machine Translation
Yusen Lin, Jiayong Lin, Shuaicheng Zhang +1
Recent studies have demonstrated a perceivable improvement on the performance of neural machine translation by applying cross-lingual language model pretraining (Lample and Conneau…