A Comprehensive Survey on Deep Graph Representation Learning

Graph representation learning aims to effectively encode high-dimensional sparse graph-structured data into low-dimensional dense vectors, which is a fundamental task that has been widely studied in a range of fields, including machine learning and data mining. Classic graph embedding methods follow the basic idea that the embedding vectors of interconnected nodes in the graph can still maintain a relatively close distance, thereby preserving the structural information between the nodes in the graph. However, this is sub-optimal due to: (i) traditional methods have limited model capacity which limits the learning performance; (ii) existing techniques typically rely on unsupervised learning strategies and fail to couple with the latest learning paradigms; (iii) representation learning and downstream tasks are dependent on each other which should be jointly enhanced. With the remarkable success of deep learning, deep graph representation learning has shown great potential and advantages over shallow (traditional) methods, there exist a large number of deep graph representation learning techniques have been proposed in the past decade, especially graph neural networks. In this survey, we conduct a comprehensive survey on current deep graph representation learning algorithms by proposing a new taxonomy of existing state-of-the-art literature. Specifically, we systematically summarize the essential components of graph representation learning and categorize existing approaches by the ways of graph neural network architectures and the most recent advanced learning paradigms. Moreover, this survey also provides the practical and promising applications of deep graph representation learning. Last but not least, we state new perspectives and suggest challenging directions which deserve further investigations in the future

MDSC‐Net: A multi‐scale depthwise separable convolutional neural network for skin lesion segmentation

Abstract Accurate segmentation of the skin lesion region is crucial for diagnosing and screening skin diseases. However, skin lesion segmentation is challenging due to the indistinguishable boundaries of the lesion region, irregular shapes and hair interference. To settle the above issues, we propose a Multi‐scale Depthwise Separable Convolutional Neural Network for skin lesion segmentation named MDSC‐Net. Specifically, a novel Multi‐scale Depthwise Separable Residual Convolution Module is employed in skip connection, conveying more detailed features to the decoder. To compensate for the loss of spatial location information in down‐sampling, we propose a novel Spatial Adaption Module. Furthermore, we propose a Multi‐scale Decoding Fusion Module in the decoder to capture contextual information. We have performed extensive experiments to verify the effectiveness and robustness of the proposed network on three public benchmark skin lesion segmentation datasets and one public benchmark polyp segmentation dataset, including ISIC‐2017, ISIC‐2018, PH2, and Kvasir‐SEG datasets. Experimental results consistently demonstrate the proposed MDSC‐Net achieves superior segmentation across five popularly used evaluation criteria. The proposed network reaches high‐performance skin lesion segmentation, and can provide important clues to help doctors diagnose and treat skin cancer early

Spatio-Temporal Prediction of Ground-Level Ozone Concentration Based on Bayesian Maximum Entropy by Combining Monitoring and Satellite Data

Ozone (O3) pollution is one of the predominant environmental problems, and exposure to high O3 concentrations has a significant negative influence on both human health and ecosystems. Therefore, it is essential to analyze spatio-temporal characteristics of O3 distribution and to evaluate O3 exposure levels. In this study, O3 monitoring and satellite data were used to estimate O3 daily, seasonal and one-year exposure levels based on the Bayesian maximum entropy (BME) model with a spatial resolution of 1 km × 1 km in the Beijing-Tianjin-Hebei (BTH) region, China. Leave-one-out cross-validation (LOOCV) results showed that R2 for daily and one-year exposure levels were 0.81 and 0.69, respectively, and the corresponding values for RMSE were 19.58 μg/m3 and 4.40 μg/m3, respectively. The simulation results showed that the heavily polluted areas included Tianjin, Cangzhou, Hengshui, Xingtai, and Handan, while the clean areas were mainly located in Chengde, Qinhuangdao, Baoding, and Zhangjiakou. O3 pollution in summer was the most severe with an average concentration of 134.5 μg/m3. In summer, O3 concentrations in 87.7% of the grids were more than 100 μg/m3. In contrast, winter was the cleanest season in the BTH region, with an average concentration of 51.1 μg/m3

Spatio-Temporal Prediction of Ground-Level Ozone Concentration Based on Bayesian Maximum Entropy by Combining Monitoring and Satellite Data

Ozone (O3) pollution is one of the predominant environmental problems, and exposure to high O3 concentrations has a significant negative influence on both human health and ecosystems. Therefore, it is essential to analyze spatio-temporal characteristics of O3 distribution and to evaluate O3 exposure levels. In this study, O3 monitoring and satellite data were used to estimate O3 daily, seasonal and one-year exposure levels based on the Bayesian maximum entropy (BME) model with a spatial resolution of 1 km × 1 km in the Beijing-Tianjin-Hebei (BTH) region, China. Leave-one-out cross-validation (LOOCV) results showed that R2 for daily and one-year exposure levels were 0.81 and 0.69, respectively, and the corresponding values for RMSE were 19.58 μg/m3 and 4.40 μg/m3, respectively. The simulation results showed that the heavily polluted areas included Tianjin, Cangzhou, Hengshui, Xingtai, and Handan, while the clean areas were mainly located in Chengde, Qinhuangdao, Baoding, and Zhangjiakou. O3 pollution in summer was the most severe with an average concentration of 134.5 μg/m3. In summer, O3 concentrations in 87.7% of the grids were more than 100 μg/m3. In contrast, winter was the cleanest season in the BTH region, with an average concentration of 51.1 μg/m3

Estimation of Short-Term and Long-Term Ozone Exposure Levels in Beijing–Tianjin–Hebei Region Based on Geographically Weighted Regression Model

In recent years, ozone (O3) concentration has shown a decreasing trend in the Beijing–Tianjin–Hebei (BTH) region in China. However, O3 pollution remains a prominent problem. Accurate estimation of O3 exposure levels can provide support for epidemiological studies. A total of 13 variables were combined to estimate short- and long-term O3 exposure levels using the geographically weighted regression (GWR) model in the BTH region with a spatial resolution of 1 × 1 km from 2017 to 2020. Five variables were left in the GWR model. O3 concentration was positively correlated with temperature, wind speed, and SO2, whereas is was negatively correlated with precipitation and NO2. Results showed that the model performed well. Leave-one-out cross-validation (LOOCV) R2 for short- and long-term simulation results were 0.91 and 0.71, and the values for RMSE were 11.14 and 3.49 μg/m3, respectively. The annual maximum 8 h average O3 concentration was the highest in 2018 and the lowest in 2020. Decreasing concentrations of major precursors of O3 due to the regional joint prevention and control may be the reason. O3 concentration was high in the southeast of the BTH region, including in Hengshui, Handan, Xingtai and Cangzhou