Reinforcement Graph Clustering with Unknown Cluster Number

Liu, Yue; Liang, Ke; Xia, Jun; Yang, Xihong; Zhou, Sihang; Liu, Meng; Liu, Xinwang; Li, Stan Z.

doi:10.1145/3581783.3612155

Cited by 4 publications

(1 citation statement)

References 37 publications

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“…More recently, some hybrid architectures (Rong et al, 2020;Ying et al, 2021;Min et al, 2022) of GNNs and transformers are emerging to capture the topological structures of molecular graphs. Additionally, given that the available labels for molecules are often expensive or incorrect (Xia et al, 2021;Tan et al, 2021;Xia et al, 2022a), the emerging self-supervised pre-training strategies (You et al, 2020;Xia et al, 2022c;Yue et al, 2022;Liu et al, 2023) on graph-structured data are promising for molecular graph data (Hu et al, 2020;Xia et al, 2023a;Gao et al, 2022), just like the overwhelming success of pre-trained language models in natural language processing community (Devlin et al, 2019;Zheng et al, 2022).…”

Section: C3 2d and 3d Graph-based Molecular Descriptorsmentioning

confidence: 99%

Why Deep Models Often Cannot Beat Non-deep Counterparts on Molecular Property Prediction?

Xia

Zhang

Zhu

et al. 2023

Preprint

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Molecular property prediction (MPP) is a crucial task in the drug discovery pipeline, which has recently gained considerable attention thanks to advances in deep neural networks. However, recent research has revealed that deep models struggle to beat traditional non-deep ones on MPP. In this study, we benchmark 12 representative models (3 non-deep models and 9 deep models) on 14 molecule datasets. Through the most comprehensive study to date, we make the following key observations: \textbf{(\romannumeral 1)} Deep models are generally unable to outperform non-deep ones; \textbf{(\romannumeral 2)} The failure of deep models on MPP cannot be solely attributed to the small size of molecular datasets. What matters is the irregular molecule data pattern; \textbf{(\romannumeral 3)} In particular, tree models using molecular fingerprints as inputs tend to perform better than other competitors. Furthermore, we conduct extensive empirical investigations into the unique patterns of molecule data and inductive biases of various models underlying these phenomena.

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