3 citations · 6 across the 6 of their papers we have counts for
7 papers
Empowering LLMs for Structure-Based Drug Design via Exploration-Augmented Latent Inference
Xuanning Hu, Anchen Li, Qianli Xing +3
Large Language Models (LLMs) possess strong representation and reasoning capabilities, but their application to structure-based drug design (SBDD) is limited by insufficient unders…
When Noisy Labels Meet Class Imbalance on Graphs: A Graph Augmentation Method with LLM and Pseudo Label
Riting Xia, Rucong Wang, Yulin Liu +3
Class-imbalanced graph node classification is a practical yet underexplored research problem. Although recent studies have attempted to address this issue, they typically assume cl…
Incomplete Graph Learning: A Comprehensive Survey
Riting Xia, Huibo Liu, Anchen Li +4
Graph learning is a prevalent field that operates on ubiquitous graph data. Effective graph learning methods can extract valuable information from graphs. However, these methods ar…
A Novel Neural-symbolic System under Statistical Relational Learning
Dongran Yu, Xueyan Liu, Shirui Pan +2
A key objective in the field of artificial intelligence is to develop cognitive models that can exhibit human-like intellectual capabilities. One promising approach to achieving th…
CDRec: Cayley-Dickson Recommender
Anchen Li, Bo Yang, Huan Huo +1
In this paper, we propose a recommendation framework named Cayley-Dickson Recommender. We introduce Cayley-Dickson construction which uses a recursive process to define hypercomple…
Hyperbolic Neural Collaborative Recommender
Anchen Li, Bo Yang, Hongxu Chen +1
This paper explores the use of hyperbolic geometry and deep learning techniques for recommendation. We present Hyperbolic Neural Collaborative Recommender (HNCR), a deep hyperbolic…
HSR: Hyperbolic Social Recommender
Anchen Li, Bo Yang
With the prevalence of online social media, users' social connections have been widely studied and utilized to enhance the performance of recommender systems. In this paper, we exp…