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preprint · ArXiv.org · 2025

Molecular Graph Contrastive Learning with Line Graph

Xueyuan Chen, Shangzhe Li, Ruomei Liu, Bowen Shi, Jiaheng Liu, Junran Wu, Ke Xu

Cited by 0Open access

Abstract

Trapped by the label scarcity in molecular property prediction and drug design, graph contrastive learning (GCL) came forward. Leading contrastive learning works show two kinds of view generators, that is, random or learnable data corruption and domain knowledge incorporation. While effective, the two ways also lead to molecular semantics altering and limited generalization capability, respectively. To this end, we relate the \textbf{L}in\textbf{E} graph with \textbf{MO}lecular graph co\textbf{N}trastive learning and propose a novel method termed \textit{LEMON}. Specifically, by contrasting the given graph with the corresponding line graph, the graph encoder can freely encode the molecular semantics without omission. Furthermore, we present a new patch with edge attribute fusion and two local contrastive losses enhance information transmission and tackle hard negative samples. Compared with state-of-the-art (SOTA) methods for view generation, superior performance on molecular property prediction suggests the effectiveness of our proposed framework.

Cite this paper

APA 7

Chen, X., Li, S., Liu, R., Shi, B., Liu, J., Wu, J., & Xu, K. (2025). Molecular Graph Contrastive Learning with Line Graph. ArXiv.org.

MLA 9

Chen, Xueyuan, et al. “Molecular Graph Contrastive Learning with Line Graph.” ArXiv.org, 2025.

Chicago

Chen, Xueyuan, Shangzhe Li, Ruomei Liu, Bowen Shi, Jiaheng Liu, Junran Wu, and Ke Xu. 2025. “Molecular Graph Contrastive Learning with Line Graph.” ArXiv.org.

Harvard

Chen, X. et al. (2025) ‘Molecular Graph Contrastive Learning with Line Graph’, ArXiv.org.

BibTeX
@misc{chen2025molecular,
  title = {{Molecular Graph Contrastive Learning with Line Graph}},
  author = {Chen, Xueyuan and Li, Shangzhe and Liu, Ruomei and Shi, Bowen and Liu, Jiaheng and Wu, Junran and Xu, Ke},
  journal = {ArXiv.org},
  year = {2025}
}

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