activity
20222025
most citedSimGRACE: A Simple Framework for Graph Contrastive Learning without Data Augmentation

294 citations · 348 across the 16 of their papers we have counts for

collaborators
Showing cs.LGShow all

6 papers · 1 filter

cs.LG2025★ 1 cited

Multimodal Regression for Enzyme Turnover Rates Prediction

Bozhen Hu, Cheng Tan, Siyuan Li +4

The enzyme turnover rate is a fundamental parameter in enzyme kinetics, reflecting the catalytic efficiency of enzymes. However, enzyme turnover rates remain scarce across most org…

cs.LG2024

MeToken: Uniform Micro-environment Token Boosts Post-Translational Modification Prediction

Cheng Tan, Zhenxiao Cao, Zhangyang Gao +6

Post-translational modifications (PTMs) profoundly expand the complexity and functionality of the proteome, regulating protein attributes and interactions that are crucial for biol…

cs.LG2024★ 1 cited

FlexMol: A Flexible Toolkit for Benchmarking Molecular Relational Learning

Sizhe Liu, Jun Xia, Lecheng Zhang +8

Molecular relational learning (MRL) is crucial for understanding the interaction behaviors between molecular pairs, a critical aspect of drug discovery and development. However, th…

cs.LG2024★ 1 cited

A Graph is Worth Words: Euclideanizing Graph using Pure Transformer

Zhangyang Gao, Daize Dong, Cheng Tan +3

Can we model Non-Euclidean graphs as pure language or even Euclidean vectors while retaining their inherent information? The Non-Euclidean property have posed a long term challenge…

cs.LG2024

Deep Manifold Graph Auto-Encoder for Attributed Graph Embedding

Bozhen Hu, Zelin Zang, Jun Xia +3

Representing graph data in a low-dimensional space for subsequent tasks is the purpose of attributed graph embedding. Most existing neural network approaches learn latent represent…

cs.LG2022★ 294 cited

SimGRACE: A Simple Framework for Graph Contrastive Learning without Data Augmentation

Jun Xia, Lirong Wu, Jintao Chen +2

Graph contrastive learning (GCL) has emerged as a dominant technique for graph representation learning which maximizes the mutual information between paired graph augmentations tha…