热点文献带您关注AI图神经网络的最新进展——图书馆前沿文献专题推荐服务（69）

2022-09-05

 

 

        在第67期AI文献推荐中，我们为您推荐了深度强化学习的热点文献，包括通过深度强化学习与混合场景训练相结合在赛车游戏中超越人类车手，基于深度强化学习实现托卡马克磁控制器设计，增强、增量和跨语言的社交事件检测体系结构，以及基于深度强化学习的对抗性图像隐写术。
        本期我们为您选取了4篇文献，介绍图神经网络的最新发展前沿，包括利用GNN量化城市道路网的空间同质性，基于图神经网络对分子表征进行分子对比学习，基于GNN的深度学习框架快速预测共晶体的形成，以及图神经网络的自监督学习综述，推送给相关领域的科研人员。
文献一  利用GNN量化城市道路网的空间同质性
Quantifying the spatial homogeneity of urban road networks via graph neural networks
Jiawei Xue, etc.
NATURE MACHINE INTELLIGENCE, 2022, 4(3): 246-257
Quantifying the topological similarities of different parts of urban road networks enables us to understand urban growth patterns. Although conventional statistics provide useful information about the characteristics of either a single node’s direct neighbours or the entire network, such metrics fail to measure the similarities of subnetworks or capture local, indirect neighbourhood relationships. Here we propose a graph-based machine learning method to quantify the spatial homogeneity of subnetworks. We apply the method to 11,790 urban road networks across 30 cities worldwide to measure the spatial homogeneity of road networks within each city and across different cities. We find that intracity spatial homogeneity is highly associated with socioeconomic status indicators such as gross domestic product and population growth. Moreover, intercity spatial homogeneity values obtained by transferring the model across different cities reveal the intercity similarity of urban network structures originating in Europe, passed on to cities in the United States and Asia. The socioeconomic development and intercity similarity revealed using our method can be leveraged to understand and transfer insights between cities. It also enables us to address urban policy challenges including network planning in rapidly urbanizing areas and regional inequality.
阅读原文：https://www.nature.com/articles/s42256-022-00462-y
 
                                                               Spatial homogeneity
文献二 基于图神经网络对分子表征进行分子对比学习
Molecular contrastive learning of representations via graph neural networks
Yuyang Wang, etc.
NATURE MACHINE INTELLIGENCE, 2022, 4(3): 279-287
Molecular machine learning bears promise for efficient molecular property prediction and drug discovery. However, labelled molecule data can be expensive and time consuming to acquire. Due to the limited labelled data, it is a great challenge for supervised-learning machine learning models to generalize to the giant chemical space. Here we present MolCLR (Molecular Contrastive Learning of Representations via Graph Neural Networks), a self-supervised learning framework that leverages large unlabelled data (~10 million unique molecules). In MolCLR pre-training, we build molecule graphs and develop graph-neural-network encoders to learn differentiable representations. Three molecule graph augmentations are proposed: atom masking, bond deletion and subgraph removal. A contrastive estimator maximizes the agreement of augmentations from the same molecule while minimizing the agreement of different molecules. Experiments show that our contrastive learning framework significantly improves the performance of graph-neural-network encoders on various molecular property benchmarks including both classification and regression tasks. Benefiting from pre-training on the large unlabelled database, MolCLR even achieves state of the art on several challenging benchmarks after fine-tuning. In addition, further investigations demonstrate that MolCLR learns to embed molecules into representations that can distinguish chemically reasonable molecular similarities.
阅读原文：https://www.nature.com/articles/s42256-022-00447-x
 

 
                                                                   Overview of MolCLR
文献三 基于GNN的深度学习框架快速预测共晶体的形成
Coupling complementary strategy to flexible graph neural network for quick discovery of coformer in diverse co-crystal materials
Yuanyuan Jiang, etc.
NATURE COMMUNICATIONS, 2021, 12(1)
Cocrystal engineering have been widely applied in pharmaceutical, chemistry and material fields. However, how to effectively choose coformer has been a challenging task on experiments. Here we develop a graph neural network (GNN) based deep learning framework to quickly predict formation of the cocrystal. In order to capture main driving force to crystallization from 6819 positive and 1052 negative samples reported by experiments, a feasible GNN framework is explored to integrate important prior knowledge into end-to-end learning on the molecular graph. The model is strongly validated against seven competitive models and three challenging independent test sets involving pharmaceutical cocrystals, pi-pi cocrystals and energetic cocrystals, exhibiting superior performance with accuracy higher than 96%, confirming its robustness and generalization. Furthermore, one new energetic cocrystal predicted is successfully synthesized, showcasing high potential of the model in practice. All the data and source codes are available at for aiding cocrystal community.
Experimental determination of new cocrystals remains challenging due to the need of a systematic screening with a large range of coformers. Here the authors develop a flexible deep learning framework based on graph neural network demonstrated to quickly predict the formation of co-crystals.
阅读原文：https://www.nature.com/articles/s41467-021-26226-7
 

                            Overview of CCGNet cocrystal-screening framework
 
文献四 综述：图神经网络的自监督学习
Self-Supervised Learning of Graph Neural Networks: A Unified Review
Yaochen Xie, etc.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2022
Deep models trained in supervised mode have achieved remarkable success on a variety of tasks. When labeled samples are limited, self-supervised learning (SSL) is emerging as a new paradigm for making use of large amounts of unlabeled samples. SSL has achieved promising performance on natural language and image learning tasks. Recently, there is a trend to extend such success to graph data using graph neural networks (GNNs). In this survey, we provide a unified review of different ways of training GNNs using SSL. Specifically, we categorize SSL methods into contrastive and predictive models. In either category, we provide a unified framework for methods as well as how these methods differ in each component under the framework. Our unified treatment of SSL methods for GNNs sheds light on the similarities and differences of various methods, setting the stage for developing new methods and algorithms. We also summarize different SSL settings and the corresponding datasets used in each setting. To facilitate methodological development and empirical comparison, we develop a standardized testbed for SSL in GNNs, including implementations of common baseline methods, datasets, and evaluation metrics.
阅读原文：https://ieeexplore.ieee.org/document/9764632
 

                           An overview of self-supervised learning methods

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