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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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CDA-SKAG: Predicting circRNA-disease associations using similarity kernel fusion and an attention-enhancing graph autoencoder

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MATHEMATICAL BIOSCIENCES AND ENGINEERING 2023年第5期20卷 7957-7980页

作者： Wang, Huiqing Han, Jiale Li, Haolin Duan, Liguo Liu, Zhihao Cheng, Hao Taiyuan Univ Technol Coll Informat & Comp Taiyuan 030024 Peoples R China

Circular RNAs (circRNAs) constitute a category of circular non-coding RNA molecules whose abnormal expression is closely associated with the development of diseases. As biological data become abundant, a lot of computational prediction models have been used for circRNA-disease association prediction. However, existing prediction models ignore the non-linear information of circRNAs and diseases when fusing multi-source similarities. In addition, these models fail to take full advantage of the vital feature information of high-similarity neighbor nodes when extracting features of circRNAs or diseases. In this paper, we propose a deep learning model, CDA-SKAG, which introduces a similarity kernel fusion algorithm to integrate multi-source similarity matrices to capture the non-linear information of circRNAs or diseases, and construct a circRNA information space and a disease information space. The model embeds an attention-enhancing layer in the graph autoencoder to enhance the associations between nodes with higher similarity. A cost-sensitive neural network is introduced to address the problem of positive and negative sample imbalance, consequently improving our model's generalization capability. The experimental results show that the prediction performance of our model CDA-SKAG outperformed existing circRNA-disease association prediction models. The results of the case studies on lung and cervical cancer suggest that CDA-SKAG can be utilized as an effective tool to assist in predicting circRNA-disease associations.

关键词： circRNA-disease association prediction similarity kernel fusion graph autoencoder attention mechanism cost-sensitive neural network

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SIGA: social influence modeling integrating graph autoencoder for rating prediction

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APPLIED INTELLIGENCE 2023年第6期53卷 6432-6447页

作者： Liu, Jinxin Xiao, Yingyuan Zheng, Wenguang Hsu, Ching-Hsien Tianjin Univ Technol Engn Res Ctr Learning Based Intelligent Syst Minist Educ Tianjin 300384 Peoples R China Tianjin Univ Technol Tianjin Key Lab Intelligence Comp & Novel Softwar Tianjin 300384 Peoples R China Asia Univ Coll Informat & Elect Engn Taichung 41354 Taiwan

With the revival of social networks, many studies try to integrate social relations of users to improve the accuracy of rating prediction. However, most existing methods cannot accurately reflect how social relations affect user preferences. The main reason is that these methods only investigate the directly or indirectly reachable nodes in social networks, while ignoring global transitivity of influence. Actually, for a particular user, his preference will not only affect his neighbors but also some people he does not know, especially when the person is an opinion leader. In this paper, we propose a social influence model, SIGA, which integrates graph autoencoder and is used for the rating prediction task. First, we establish a new method to quantify the social influence of users from the perspective of information dissemination in social networks. Second, we employ graph autoencoder (GAE) to model the interaction between users and items from the view of message passing on bipartite graph, which can obtain high-quality representations of users and items. By building a hybrid architecture of social modeling and GAE, it is expected to be endowed with both benefits from them. In addition, our model is interpretable at both the structural level and attribute level. Extensive experimental results on four real-world datasets have shown the effectiveness and generalization of the proposed model.

关键词： Social influence Recommendation system Rating prediction graph autoencoder

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VGAEDTI: drug-target interaction prediction based on variational inference and graph autoencoder

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BMC BIOINFORMATICS 2023年第1期24卷 1-17页

作者： Zhang, Yuanyuan Feng, Yinfei Wu, Mengjie Deng, Zengqian Wang, Shudong Yinfei Feng Qingdao Univ Technol Qingdao Peoples R China China Univ Petr Sch Comp Sci & Technol Qingdao Peoples R China

MotivationAccurate identification of Drug-Target Interactions (DTIs) plays a crucial role in many stages of drug development and drug repurposing. (i) Traditional methods do not consider the use of multi-source data and do not consider the complex relationship between data sources. (ii) How to better mine the hidden features of drug and target space from high-dimensional data, and better solve the accuracy and robustness of the *** solve the above problems, a novel prediction model named VGAEDTI is proposed in this paper. We constructed a heterogeneous network with multiple sources of information using multiple types of drug and target dataIn order to obtain deeper features of drugs and targets, we use two different autoencoders. One is variational graph autoencoder (VGAE) which is used to infer feature representations from drug and target spaces. The second is graph autoencoder (GAE) propagating labels between known DTIs. Experimental results on two public datasets show that the prediction accuracy of VGAEDTI is better than that of six DTIs prediction methods. These results indicate that model can predict new DTIs and provide an effective tool for accelerating drug development and repurposing.

关键词： Drug-target interaction prediction Variational inference graph autoencoder Variational expected maximum algorithm Drug repurposing

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Robustness meets accuracy in adversarial training for graph autoencoder

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NEURAL NETWORKS 2023年 157卷 114-124页

作者： Zhou, Xianchen Hu, Kun Wang, Hongxia Natl Univ Def Technol Coll Liberal Arts & Sci Changsha 410072 Hunan Peoples R China Natl Univ Def Technol Coll Comp Sci & Technol Changsha 410072 Hunan Peoples R China

graph autoencoder (GAE) is an effective deep method for graph embedding, while it is vulnerable to the graph adversarial attacks. Adversarial training, which generates adversarial examples in the adversarial scope(neighborhood of natural examples), is effective to improve the robustness of GAE. However, it may lead to degradation of natural accuracy (accuracy on natural examples) due to the extra training examples generated in the adversarial scope (the reasonable scope of adversarial examples). Therefore, considering robustness and natural accuracy is crucial to GAE. In this paper, an improved GAE model is formulated by combining the Structure and Feature encoders, and a novel Adversarial Training strategy (GAE-SFAT) is proposed based on improved GAE. GAE-SFAT has a smaller but more reasonable adversarial scope for adversarial training, which keeps the robustness and reduces the degradation of natural accuracy compared with ordinary adversarial training. In addition, a novel algorithm considering the robustness and accuracy is designed to optimize the GAE-SFAT. We conduct experiments both on the natural graphs as well as perturbed graphs for three datasets. The results show that GAE-SFAT can perform better than state of arts adversarial training model under different kinds of perturbations. (c) 2022 Elsevier Ltd. All rights reserved.

关键词： graph embedding Adversarial training graph autoencoder Robustness

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Multi-view representation model based on graph autoencoder

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INFORMATION SCIENCES 2023年第1期632卷 439-453页

作者： Li, Jingci Lu, Guangquan Wu, Zhengtian Ling, Fuqing Guangxi Normal Univ Key Lab Educ Blockchain & Intelligent Technol Minist Educ Guilin 541004 Peoples R China Guangxi Normal Univ Guangxi Key Lab Multisource Informat Min & Secur Guilin 541004 Peoples R China Suzhou Univ Sci & Technol Elect & Informat Engn Suzhou 215009 Peoples R China

graph representation learning is a hot topic in non-Euclidean data in various domains, such as social networks, biological networks, etc. When some data labels are missing, graph autoencoder and graph variational autoencoder can perform outstanding abilities on node clustering or link prediction tasks. However, most existing graph representation learning ignores data's multi-modal features and takes the node features and graph structure features as one view. Besides, most graph autoencoders only reconstruct the node feature matrix or adjacency matrix, which does not fully use the hidden representation information. In this paper, we propose a multi-view representation model based on graph autoencoder, which can employ the global structure topology, latent local topology, and feature relative information. Meanwhile, we add another decoder to reconstruct the node feature matrix as an auxiliary task. In this way, the proposed framework can utilize the learned representation sufficiently. We validate the effectiveness of our framework on four datasets, and the experimental results demonstrate superior performance compared with other advanced frameworks.

关键词： graph representation learning graph autoencoder Unsupervised learning Multi-view learning

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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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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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