51 citations · 157 across the 14 of their papers we have counts for
16 papers
LLM: Multi-view Molecular Representation Learning with Large Language Models
Jiaxin Ju, Yizhen Zheng, Huan Yee Koh +2
Accurate molecular property prediction is a critical challenge with wide-ranging applications in chemistry, materials science, and drug discovery. Molecular representation methods,…
ModuLM: Enabling Modular and Multimodal Molecular Relational Learning with Large Language Models
Zhuo Chen, Yizhen Zheng, Huan Yee Koh +4
Molecular Relational Learning (MRL) aims to understand interactions between molecular pairs, playing a critical role in advancing biochemical research. With the recent development…
A Label-Free Heterophily-Guided Approach for Unsupervised Graph Fraud Detection
Junjun Pan, Yixin Liu, Xin Zheng +4
Graph fraud detection (GFD) has rapidly advanced in protecting online services by identifying malicious fraudsters. Recent supervised GFD research highlights that heterophilic conn…
Collaborative Expert LLMs Guided Multi-Objective Molecular Optimization
Jiajun Yu, Yizhen Zheng, Huan Yee Koh +3
Molecular optimization is a crucial yet complex and time-intensive process that often acts as a bottleneck for drug development. Traditional methods rely heavily on trial and error…
Large Language Models in Drug Discovery and Development: From Disease Mechanisms to Clinical Trials
Yizhen Zheng, Huan Yee Koh, Maddie Yang +5
The integration of Large Language Models (LLMs) into the drug discovery and development field marks a significant paradigm shift, offering novel methodologies for understanding dis…
PREM: A Simple Yet Effective Approach for Node-Level Graph Anomaly Detection
Junjun Pan, Yixin Liu, Yizhen Zheng +1
Node-level graph anomaly detection (GAD) plays a critical role in identifying anomalous nodes from graph-structured data in various domains such as medicine, social networks, and e…
Large Language Models for Scientific Synthesis, Inference and Explanation
Yizhen Zheng, Huan Yee Koh, Jiaxin Ju +4
Large language models are a form of artificial intelligence systems whose primary knowledge consists of the statistical patterns, semantic relationships, and syntactical structures…
Integrating Graphs with Large Language Models: Methods and Prospects
Shirui Pan, Yizhen Zheng, Yixin Liu
Large language models (LLMs) such as GPT-4 have emerged as frontrunners, showcasing unparalleled prowess in diverse applications, including answering queries, code generation, and…
A Survey on Fairness-aware Recommender Systems
Di Jin, Luzhi Wang, He Zhang +4
As information filtering services, recommender systems have extremely enriched our daily life by providing personalized suggestions and facilitating people in decision-making, whic…
Dual Intent Enhanced Graph Neural Network for Session-based New Item Recommendation
Di Jin, Luzhi Wang, Yizhen Zheng +5
Recommender systems are essential to various fields, e.g., e-commerce, e-learning, and streaming media. At present, graph neural networks (GNNs) for session-based recommendations n…
Beyond Smoothing: Unsupervised Graph Representation Learning with Edge Heterophily Discriminating
Yixin Liu, Yizhen Zheng, Daokun Zhang +2
Unsupervised graph representation learning (UGRL) has drawn increasing research attention and achieved promising results in several graph analytic tasks. Relying on the homophily a…
Unifying Graph Contrastive Learning with Flexible Contextual Scopes
Yizhen Zheng, Yu Zheng, Xiaofei Zhou +3
Graph contrastive learning (GCL) has recently emerged as an effective learning paradigm to alleviate the reliance on labelling information for graph representation learning. The co…
Rethinking and Scaling Up Graph Contrastive Learning: An Extremely Efficient Approach with Group Discrimination
Yizhen Zheng, Shirui Pan, Vincent Cs Lee +2
Graph contrastive learning (GCL) alleviates the heavy reliance on label information for graph representation learning (GRL) via self-supervised learning schemes. The core idea is t…
CGMN: A Contrastive Graph Matching Network for Self-Supervised Graph Similarity Learning
Di Jin, Luzhi Wang, Yizhen Zheng +4
Graph similarity learning refers to calculating the similarity score between two graphs, which is required in many realistic applications, such as visual tracking, graph classifica…
Towards Graph Self-Supervised Learning with Contrastive Adjusted Zooming
Yizhen Zheng, Ming Jin, Shirui Pan +4
Graph representation learning (GRL) is critical for graph-structured data analysis. However, most of the existing graph neural networks (GNNs) heavily rely on labeling information,…
Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning
Ming Jin, Yizhen Zheng, Yuan-Fang Li +3
Graph representation learning plays a vital role in processing graph-structured data. However, prior arts on graph representation learning heavily rely on labeling information. To…