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A deep learning approach for polyline and building simplification based on graph autoencoder with flexible constraints

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CARTOgraphY AND GEOgraphIC INFORMATION SCIENCE 2024年第1期51卷 79-96页

作者： Yan, Xiongfeng Yang, Min Tongji Univ Coll Surveying & Geoinformat Shanghai Peoples R China Wuhan Univ Sch Resource & Environm Sci Wuhan Peoples R China

Polyline and building simplification remain challenging in cartography. Most proposed algorithms are geometric-based and rely on specific rules. In this study, we propose a deep learning approach to simplify polylines and buildings based on a graph autoencoder (GAE). The model receives the coordinates of line vertices as inputs and obtains a simplified representation by reconstructing the original inputs with fewer vertices through pooling, in which the graph convolution based on graph Fourier transform is used for the layer-by-layer feature computation. By adjusting the loss functions, constraints such as area and shape preservation and angle-characteristic enhancement are flexibly configured under a unified learning framework. Our results confirmed the applicability of the GAE approach to the multi-scale simplification of land-cover boundaries and contours by adjusting the number of output nodes. Compared with existing Douglas-Peukcer, Fourier transform, and Delaunay triangulation approaches, the GAE approach was superior in achieving morphological abstraction while producing reasonably low position, area, and shape changes. Furthermore, we applied it to simplify buildings and demonstrated the potential for preserving the diversified characteristics of different types of lines.

关键词： Polyline simplification building simplification graph autoencoder deep learning map generalization

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Multi-sample dual-decoder graph autoencoder

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METHODS 2023年第1期211卷 31-41页

作者： He, Mengyao Zhao, Qingqing Zhang, Han Nankai Univ Coll Artificial Intelligence Tongyan Rd Tianjin 300350 Peoples R China

Self-supervised learning has shown superior performance on graph-related tasks in recent years. The most advanced methods are based on contrast learning, which severely limited by structured data augmentation techniques and complex training methods. Generative self-supervised learning, especially graph autoencoders (GAEs), can prevent the above dependence and has been demonstrated as an effective approach. In addition, most previous works only reconstruct the graph topological structure or node features. Few works consider both and combine them together to obtain their complementary information. To overcome these problems, we propose a generative self-supervised graph representation learning methodology named Multi -View Dual-decoder graph autoencoder (MDGA). Specifically, we first design a multi-sample graph learning strategy which benefits the generalization of the dual-decoder graph autoencoder. Moreover, the proposed model reconstructs the graph topological structure with a traditional GAE and extracts node attributes by masked feature reconstruction. Experimental results on five public benchmark datasets demonstrate that MDGA outperforms state-of-the-art methods in both node classification and link prediction tasks.

关键词： graph neural networks graph representation learning Self-supervised learning graph autoencoder

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Predicting Drug Synergy and Discovering New Drug Combinations Based on a graph autoencoder and Convolutional Neural Network

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INTERDISCIPLINARY SCIENCES-COMPUTATIONAL LIFE SCIENCES 2023年第2期15卷 316-330页

作者： Li, Huijun Zou, Lin Kowah, Jamal A. H. He, Dongqiong Wang, Lisheng Yuan, Mingqing Liu, Xu Guangxi Univ Sch Med Nanning 530004 Peoples R China Guangxi Univ Sch Chem & Chem Engn Nanning 530004 Peoples R China

Drug synergy is a crucial component in drug reuse since it solves the problem of sluggish drug development and the absence of corresponding drugs for several diseases. Predicting drug synergistic relationships can screen drug combinations in advance and reduce the waste of laboratory resources. In this research, we proposed a model that utilizes graph autoencoder and convolutional neural networks to predict drug synergy (GAECDS). Our methods include a graph convolutional neural network as an encoder to encode drug features and use a matrix factorization method as a decoder. Multilayer perceptron (MLP) was applied to process cell line features and combine them with drug features. Furthermore, the latent vectors generated during the encoding process are being used to predict drug synergistic scores using a convolutional neural network. By measuring prediction performance using AUC, AUPR, and F1 score, GAECDS superior to other state-of-the-art models. In addition, four pairs of the predicted top 10 drug combinations were found to work well enough for evaluation. The case study shows that the GAECDS approach is useful for identifying potential drug synergy. [graphICS] .

关键词： Drug synergy Deep learning graph autoencoder graph convolutional neural network

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INDIVIDUAL TREE SEGMENTATION FROMBLS DATA BASED ON graph autoencoder 5

INDIVIDUAL TREE SEGMENTATION FROMBLS DATA BASED ON GRAPH AUT...

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5th International-Society-for-Photogrammetry-and-Remote-Sensing (ISPRS) Geospatial Week (GSW)

作者： Fekry, Reda Yao, Wei Sani-Mohammed, Abubakar Amr, Doha Benha Univ Dept Geomat Fac Engn Shoubra Cairo Egypt Hong Kong Polytechn Univ Dept Land Surveying & Geo Informat Hong Kong Peoples R China

In the last two decades, Light detection and ranging (LiDAR) has been widely employed in forestry applications. Individual tree segmentation is essential to forest management because it is a prerequisite to tree reconstruction and biomass estimation. This paper introduces a general framework to extract individual trees from the LiDAR point cloud based on a graph link prediction problem. First, an undirected graph is generated from the point cloud based on K-nearest neighbors (KNN). Then, this graph is used to train a convolutional autoencoder that extracts the node embeddings to reconstruct the graph. Finally, the individual trees are defined by the separate sets of connected nodes of the reconstructed graph. A key advantage of the proposed method is that no further knowledge about tree or forest structure is required. Seven sample plots from a plantation forest with poplar and dawn redwood species have been employed in the experiments. Though the precision of the experimental results is up to 95 % for poplar species and 92 % for dawn redwood trees, the method still requires more investigations on natural forest types with mixed tree species.

关键词： LiDAR Individual Tree Segmentation Backpack Laser Scanning graph Neural Network graph autoencoder

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Associating field components in heterogeneous handwritten form images using graph autoencoder 2

Associating field components in heterogeneous handwritten fo...

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15th IAPR International Conference on Document Analysis and Recognition (ICDAR) / 2nd Workshop of Machine Learning (WML)

作者： Srivastava, Divya Harit, Gaurav Indian Inst Technol Jodhpur Dept Comp Sci & Engn Jodhpur Rajasthan India

ISBN: (纸本)9781728150543

We propose a graph-based deep network for predicting the associations pertaining to field labels and field values in heterogeneous handwritten form images. We consider forms in which the field label comprises printed text and field value can be the handwritten text. Inspired by the relationship predicting capability of the graphical models, we use a graph autoencoder to perform the intended field label to field value association in a given form image. To the best of our knowledge, it is the first attempt to perform label-value association in a handwritten form image using a machine learning approach. We have prepared our handwritten form image dataset comprising 300 images from 30 different templates having 10 images per template. Our framework is experimented on different network parameter and has shown promising results.

关键词： Form Processing Document Images graph Convolution Network graph autoencoder Field Association

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Network Link Status Classification Method Based on graph autoencoder 1st

Network Link Status Classification Method Based on Graph Aut...

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1st International Conference on Emerging Networking Architecture and Technologies (ICENAT)

作者： Feng, Guoli Wang, Ning Ma, Run Wei, Wenbin Li, Xiaobo Lin, Peng State Grid Ningxia Elect Power Co Informat & Telecommun Co Yinchuan Ningxia Peoples R China Beijing Vectinfo Technol Co Ltd Beijing Peoples R China

ISBN: (纸本)9789811996962;9789811996979

In recent years, with the rapid development of the Internet, the amount of data in the network increases exponentially, and people have higher requirements on the quality of network links, which poses new challenges to network management and analysis. Network link status classification can be used to predict network link status categories by using the current network topology information and feature information, facilitating network management and analysis. However, most of the existing models consider to predict the time sequence information of the link status without considering the topology information and node attributes of the network. Therefore, we design a network link status classification model based on graph autoencoder for computer network scenarios. The attention mechanism is introduced into the encoder, and then the node vector is spliced into edge vector, which is then input into the multi-layer perceptron for classification. Finally, the feasibility and validity of the link status classification model are verified on two datasets.

关键词： Link prediction graph autoencoder Multilayer perceptron Node embedding

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Remaining useful life prediction of lithium battery based on multi decoder graph autoencoder and transformer network 7

Remaining useful life prediction of lithium battery based on...

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7th IFAC Conference on Engine and Powertrain Control, Simulation and Modeling (E-COSM)

作者： Ma, Yan Li, Jiaqi Gao, Jinwu Jilin Univ Dept Control Sci & Engn Changchun 130012 Peoples R China

Remaining useful life (RUL) of lithium-ion battery is important to maintain safe and reliable battery operation. Health indicators (HIs) are key features for predicting RUL during battery aging, whereas current methods only consider their link to capacity. In order to learn the intrinsic connection between the aging features, a RUL prediction method based on multi decoder graph autoencoder (MGAE) and transformer network is proposed, which considers both the link between aging characteristics and the link between aging characteristics and capacity degradation. First, multiple types of aging features are extracted during battery charging and discharging, and HIs are connected into a graph structure by pearson correlation analysis. Thereby, feature information with high correlation is linked through the topology of the graph. Subsequently, the feature graph and feature matrix are input to the graph autoencoder to extract deep features. In graph decoder part, this paper improves to a multi decoder in order to update and select features by the updated graph structure. Finally, new feature matrix is fed into transformer, and RUL prediction is realized by parallel processing through multi-head self-attention. The validity of proposed method is demonstrated by NASA dataset and compared with other advanced methods. The results show that our approach achieves average RE of 0.09 and maintains RMSE of 0.01. Copyright (C) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)

关键词： Lithium-ion Battery Remaining Useful Life graph autoencoder Multi Decoder Transformer Network

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Pre-LogMGAE: Identification of Log Anomalies Using a Pre-trained Masked graph autoencoder 43

Pre-LogMGAE: Identification of Log Anomalies Using a Pre-tra...

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43rd International Symposium on Reliable Distributed Systems

作者： Wu, Aming Kwon, Young-Woo Kyungpook Natl Univ Sch Comp Sci & Engn Daegu South Korea

ISBN: (纸本)9798331530044;9798331530037

Log-based anomaly detection in software systems is becoming increasingly crucial for monitoring network operations and ensuring system security. Deep learning-based methods are widely used for large-scale log anomaly detection due to their capacity to learn complex features. However, current research predominantly treats original logs as simple sequences, ignoring their complex structure and dynamic dependency relationships. Additionally, these methods often rely on extensive labeled data or domain-specific vectors to represent logs for model training, which can be labor-intensive to label manually and ineffective across various domains within a system. To address these challenges, this paper proposes Pre-LogMGAE, a universal masked graph autoencoder (GAE) framework with contrastive learning for self-supervised pre-training for log anomaly detection. In contrast to graph or link reconstruction, Pre-LogMGAE focuses on node feature reconstruction using a masking strategy to reduce the impact of excessive redundant information. Furthermore, we introduce graph Attention Networks (GAT) with the Gated Recurrent Unit (GRU) to incorporate sequence modeling, allowing for capturing long-term and short-term dependencies in log events. We include contrastive learning objectives in fine-tuning to extract diverse features and enhance the algorithm's robustness. Through an extensive evaluation of three real-world datasets and specific case studies with configuration error, Pre-LogMGAE demonstrates superior performance compared to the six baselines, including PCA, IM, DeepLog, LogRobust, LogBERT, and DeepTraLog. This superiority is evident in terms of precision, recall, F1 score, and time efficiency, highlighting Pre-LogMGAE's stability and reliability in anomaly detection. The study aims to improve anomaly detection capabilities in multi-source system logs, offering innovative technical support to enhance system security and reliability.

关键词： System Security graph autoencoder Self-Supervised Learning Contrastive Learning Anomaly Detection

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ComMGAE: Community Aware Masked graph autoencoder 33rd

ComMGAE: Community Aware Masked Graph AutoEncoder

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33rd International Conference on Artificial Neural Networks and Machine Learning (ICANN)

作者： Jiang, Gaohang Jin, Xu Luo, Mengyu Chen, Jianxia Huang, Zhongwei Wang, Jing Hubei Univ Technol Sch Comp Sci Wuhan 430068 Peoples R China

ISBN: (纸本)9783031723438;9783031723445

Independent of graph augmentation techniques, graph autoencoders (GAEs) have yielded promising results in the realm of self-supervised learning. However, GAEs tend to over-emphasize proximity information at the expense of structural information, leading to relatively poor performance on some downstream tasks such as node classification. To address this issue, we propose a novel GAE framework via community, named Community aware Masked graph autoencoder(ComMGAE). Since community represents a high-order structure of a graph, characterized by a group of densely connected nodes, ComMGAE can import structural information from community semantics to the self-supervised generative learning on the graph. Firstly, we partition the community structure by using the community detection algorithm and calculate the community strength. Then we introduce a novel community-guided graph masking strategy to learn more about the graph structure during the encoding process. Moreover, we mask and reconstruct both the structure and attribution of the graph and employ a graph neural network as the decoder to enrich learning representations with compressed information. Finally, experimental results on node classification demonstrate that the proposed ComMGAE preserves both the graph topology and semantic information effectively and outperforms other state-of-the-art baselines on a series of benchmarks.

关键词： Self-supervised learning graph generative learning graph autoencoder Community structure Mask strategy

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One2Multi graph autoencoder for Multi-view graph Clustering 20

One2Multi Graph Autoencoder for Multi-view Graph Clustering

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29th World Wide Web Conference (WWW)

作者： Fan, Shaohua Wang, Xiao Shi, Chuan Lu, Emiao Lin, Ken Wang, Bai Beijing Univ Posts & Telecommun Beijing Peoples R China Tencent Shenzhen Peoples R China

ISBN: (纸本)9781450370233

Multi-view graph clustering, which seeks a partition of the graph with multiple views that often provide more comprehensive yet complex information, has received considerable attention in recent years. Although some efforts have been made for multi-view graph clustering and achieve decent performances, most of them employ shallow model to deal with the complex relation within multi-view graph, which may seriously restrict the capacity for modeling multi-view graph information. In this paper, we make the first attempt to employ deep learning technique for attributed multi-view graph clustering, and propose a novel task-guided One2Multi graph autoencoder clustering framework. The One2Multi graph autoencoder is able to learn node embeddings by employing one informative graph view and content data to reconstruct multiple graph views. Hence, the shared feature representation of multiple graphs can be well captured. Furthermore, a self-training clustering objective is proposed to iteratively improve the clustering results. By integrating the self-training and autoencoder's reconstruction into a unified framework, our model can jointly optimize the cluster label assignments and embeddings suitable for graph clustering. Experiments on real-world attributed multi-view graph datasets well validate the effectiveness of our model.

关键词： Attributed Multi-view graph Clustering graph Convolutional Network graph autoencoder

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