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49th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Xu, Yunfeng Zhao, Shaohui Fan, Hexun Wang, Jialin Hebei Univ Sci & Technol Shijiazhuang Hebei Peoples R China

ISBN: (纸本)9798350344868;9798350344851

graph representation learning is the foundation for various graph data mining tasks. In the real world, graph data not only contains complex adjacency relationships but also diverse structural information. To address issues such as overfitting and overemphasis on neighboring information while neglecting structural information in graph autoencoders, a novel approach that combines generative learning and masked autoencoder for graph representation learning is proposed. This method employs a masked autoencoder to mask a portion of the graph structure, using the remaining structure as input to the graph autoencoder, effectively alleviating overfitting. Additionally, leveraging generative learning theory, a new graph autoencoder is introduced, capable of aggregating both neighbor and structural information to generate high-quality graph embeddings. Comparative experiments between GLMAE and representative graph representation learning methods demonstrate that GLMAE achieves state-of-the-art performance in link prediction and node classification tasks.

关键词： graph representation learning generative learning graph autoencoder link prediction node classification

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graph autoencoders for Embedding Learning in Brain Networks and Major Depressive Disorder Identification

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2024年第3期28卷 1644-1655页

作者： Noman, Fuad Ting, Chee-Ming Kang, Hakmook Phan, Raphael C. -W. Ombao, Hernando Monash Univ Malaysia Sch Informat Technol Sunway 47500 Malaysia Vanderbilt Univ Med Ctr Dept Biostat Nashville TN 37232 USA King Abdullah Univ Sci & Technol Stat Program Thuwal 239556900 Saudi Arabia

Brain functional connectivity (FC) networks inferred from functional magnetic resonance imaging (fMRI) have shown altered or aberrant brain functional connectome in various neuropsychiatric disorders. Recent application of deep neural networks to connectome-based classification mostly relies on traditional convolutional neural networks (CNNs) using input FCs on a regular Euclidean grid to learn spatial maps of brain networks neglecting the topological information of the brain networks, leading to potentially sub-optimal performance in brain disorder identification. We propose a novel graph deep learning framework that leverages non-Euclidean information inherent in the graph structure for classifying brain networks in major depressive disorder (MDD). We introduce a novel graph autoencoder (GAE) architecture, built upon graph convolutional networks (GCNs), to embed the topological structure and node content of large fMRI networks into low-dimensional representations. For constructing the brain networks, we employ the Ledoit-Wolf (LDW) shrinkage method to efficiently estimate high-dimensional FC metrics from fMRI data. We explore both supervised and unsupervised techniques for graph embedding learning. The resulting embeddings serve as feature inputs for a deep fully-connected neural network (FCNN) to distinguish MDD from healthy controls (HCs). Evaluating our model on resting-state fMRI MDD dataset, we observe that the GAE-FCNN outperforms several state-of-the-art methods for brain connectome classification, achieving the highest accuracy when using LDW-FC edges as node features. The graph embeddings of fMRI FC networks also reveal significant group differences between MDD and HCs. Our framework demonstrates the feasibility of learning graph embeddings from brain networks, providing valuable discriminative information for diagnosing brain disorders.

关键词： Functional magnetic resonance imaging Brain modeling Convolutional neural networks Deep learning Bioinformatics Sociology Image reconstruction Brain connectivity networks graph autoencoder graph convolutional network major depressive disorder resting-state fMRI

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Generative and contrastive graph representation learning with message passing

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NEURAL NETWORKS 2025年 185卷 107224页

作者： Tang, Ying Yang, Yining Sun, Guodao Zhejiang Univ Technol Coll Comp Sci & Technol Hangzhou 310000 Zhejiang Peoples R China

Self-supervised graph representation learning (SSGRL) has emerged as a promising approach for graph embeddings because it does not rely on manual labels. SSGRL methods are generally divided into generative and contrastive approaches. Generative methods often suffer from poor graph quality, while contrastive methods, which compare augmented views, are more resistant to noise. However, the performance of contrastive methods depends heavily on well-designed data augmentation and high-quality negative samples. Pure generative or contrastive methods alone cannot balance both robustness and performance. To address these issues, we propose a self-supervised graph representation learning method that integrates generative and contrastive ideas, namely Contrastive Generative Message Passing graph Learning (CGMP-GL). CGMP-GL incorporates the concept of contrast into the generative model and message aggregation module, enhancing the discriminability of node representations by aligning positive samples and separating negative samples. On one hand, CGMP-GL integrates multi-granularity topology and feature information through cross-view multi-level contrast while reconstructing masked node features. On the other hand, CGMP-GL optimizes node representations through self-supervised contrastive message passing, thereby enhancing model performance in various downstream tasks. Extensive experiments over multiple datasets and downstream tasks demonstrate the effectiveness and robustness of CGMP-GL.

关键词： Self-supervised graph representation learning graph autoencoder Contrastive learning Message passing

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Adversarial relationship graph learning soft sensor via negative information exclusion

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JOURNAL OF PROCESS CONTROL 2025年 145卷

作者： Jia, Mingwei Yang, Chao Pan, Zhouxin Liu, Qiang Liu, Yi Zhejiang Univ Technol Inst Proc Equipment & Control Engn Hangzhou 310023 Peoples R China Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Liaoning Peoples R China

The development of soft sensors in process industries necessitates learning the dynamic variable relationships caused by physicochemical reactions, whilst avoiding noise interference that degrades prediction performance and explainability. To address this, an adversarial relationship graph learning soft sensor is proposed, comprising both relationship learning and prediction modules. Irrelevant and false variable relationships caused by noise are treated as negative information, quantified through mutual information loss. They are captured by alternately adversarial training the self-attention network and graph autoencoder. By excluding negative information, a suitable variable relationship graph is constructed. The graph convolutional network then mines information from the data and relationships for accurate prediction. Two practical cases verify the model's physical consistency and demonstrate superior performance compared to several common models.

关键词： Soft sensor graph learning graph autoencoder graph convolutional network Self-attention mechanism Adversarial learning

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MM-LogVec: System Log Anomaly Detection Method Based on Multimodal Representation Learning

MM-LogVec: System Log Anomaly Detection Method Based on Mult...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Li, Jingwen Zhang, Ru Liu, Jianyi Beijing Univ. of Posts & Telecom. Beijing China

ISBN: (纸本)9798350368741

Advanced persistent threats (APTs) pose significant risks to national infrastructure and corporate security. System logs record interactions between system entities, which are widely used for APT detection. However, the complex syntax and intricate relationships in system logs pose significant challenges to the performance of deep learning models. This paper proposes MM-LogVec, a novel anomaly detection method based on multimodal feature representation. It introduces three pre-training tasks to learn joint embeddings of logs and security events, establishing semantic associations between log operations and security events through cross-modal feature alignment, thereby enhancing the understanding of complex behaviors. Additionally, a lightweight graph autoencoder is used to model interaction relationships among entities, enabling entity-level anomaly detection via feature reconstruction. Evaluations across 10 APT scenarios show MM-LogVec's exceptional performance, with a true positive rate of 98.19% and a false positive rate of 3.93%, while improving processing speed by a factor of 9 compared to the state-of-the-art AIRTAG. © 2025 IEEE.

关键词： APT detection graph autoencoder log feature representation multimodal learning

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SHGAE: Social Hypergraph autoencoder for Friendship Inference 32nd

SHGAE: Social Hypergraph AutoEncoder for Friendship Inferenc...

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32nd International Conference on Artificial Neural Networks (ICANN)

作者： Li, Yujie Chen, Yan Qi, Tianliang Tian, Feng Wu, Yaqiang Wang, Qianying Xi An Jiao Tong Univ Coll Comp Sci & Technol Xian Shaanxi Peoples R China Lenovo Res Beijing Peoples R China

ISBN: (纸本)9783031442223;9783031442230

Location-Based Social Networks (LBSNs) present a significant challenge for inferring social relationships from both social networks and user mobility. While traditional rule-based walk graph representation learning methods predict friendship based on user proximity, they fail to distinguish contributions of different mobile semantics (temporal, spatial, and activity semantics). On the other hand, graph-based autoencoder models have shown promising results, but they are not suitable for heterogeneous information in LBSNs, and they perform poorly when users lack initial features. In this paper, we propose the Social Hypergraph autoencoder (SHGAE) model, a novel autoencoder designed specifically for social hypergraphs formed by LBSNs data, which combines the strengths of these two methods. We initialize nodes vectors via hypergraph-jump-walk embedding strategy to capture features of the hypergraph, then use a well-designed autoencoder with heterogeneous message passing and attention mechanisms to model different semantic node influences. Extensive experiments demonstrate that our model outperforms state-of-the-art methods on the social relationship inference task. Moreover, in the ablation study, we find that our two proposed modules contribute differently to datasets with different sparsity, which can provide valuable insights for future research.

关键词： graph autoencoder graph Neural Networks graph attention networks Location based social network Link prediction

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Multitask joint learning with graph autoencoders for predicting potential MiRNA-drug associations

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ARTIFICIAL INTELLIGENCE IN MEDICINE 2023年 145卷 102665-102665页

作者： Zhong, Yichen Shen, Cong Xi, Xiaoting Luo, Yuxun Ding, Pingjian Luo, Lingyun Univ South China Sch Comp Sci Hengyang 421001 Peoples R China Hunan Univ Coll Comp Sci & Elect Engn Changsha 410083 Peoples R China Hunan Univ Sci & Technol Sch Comp Sci & Engn Xiangtan 411105 Peoples R China

The occurrence of many diseases is associated with miRNA abnormalities. Predicting potential drug-miRNA associations is of great importance for both disease treatment and new drug discovery. Most computation -based approaches learn one task at a time, ignoring the information contained in other tasks in the same domain. Multitask learning can effectively enhance the prediction performance of a single task by extending the valid information of related tasks. In this paper, we presented a multitask joint learning framework (MTJL) with a graph autoencoder for predicting the associations between drugs and miRNAs. First, we combined multiple pieces of information to construct a high-quality similarity network of both drugs and miRNAs and then used a graph autoencoder (GAE) to learn their embedding representations separately. Second, to further improve the embedding quality of drugs, we added an auxiliary task to classify drugs using the learned representations. Finally, the embedding representations of drugs and miRNAs were linearly transformed to obtain the predictive association scores between them. A comparison with other state-of-the-art models shows that MTJL has the best prediction performance, and ablation experiments show that the auxiliary task can enhance the embedding quality and improve the robustness of the model. In addition, we show that MTJL has high utility in predicting potential associations between drugs and miRNAs by conducting two case studies.

关键词： Drug-miRNA association Multitask learning Drug classification graph autoencoder

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WDCIP:spatio-temporal AI-driven disease control intelligent platform for combating COVID-19 pandemic

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地球空间信息科学学报（英文版） 2024年第6期27卷中插16,2023-2047页

作者： Siqi Wang Xiaoxiao Zhao Jingyu Qiu Haofen Wang Chuang Tao College of Design and Innovation Tongji UniversityShanghaiChina Product Development Department Wayz AI Technology Company LimitedShanghaiChina

The outbreak and subsequent recurring waves of COVID-19 pose threats on the emergency management and people's daily life,while the large-scale spatio-temporal epidemiological data have sure come in handy in epidemic ***,some challenges remain to be addressed in terms of multi-source heterogeneous data fusion,deep mining,and comprehensive *** Spatio-Temporal Artificial Intelligence(STAI)technology,which focuses on integrating spatial related time-series data,artificial intelligence models,and digital tools to provide intelligent computing platforms and applications,opens up new opportunities for scientific epidemic *** this end,we leverage STAI and long-term experience in location-based intelligent services in the ***,we devise and develop a STAI-driven digital infrastructure,namely,WAYZ Disease Control Intelligent Platform(WDCIP),which consists of a systematic framework for building pipelines from auto-matic spatio-temporal data collection,processing to AI-based analysis and inference imple-mentation for providing appropriate applications serving various epidemic *** to the platform implementation logic,our work can be performed and summarized from three aspects:(1)a STAI-driven integrated system;(2)a hybrid GNN-based approach for hierarchical risk assessment(as the core algorithm of WDCIP);and(3)comprehensive applica-tions for social epidemic *** work makes a pivotal contribution to facilitating the aggregation and full utilization of spatio-temporal epidemic data from multiple sources,where the real-time human mobility data generated by high-precision mobile positioning plays a vital role in sensing the spread of the *** far,WDCIP has accumulated more than 200 million users who have been served in life convenience and decision-making during the pandemic.

关键词： COVID-19 spatio-temporal artificial intelligence epidemic prevention and control platform risk assessment SIR graph autoencoder graph representation learning human mobility

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MoRGH: movie recommender system using GNNs on heterogeneous graphs

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KNOWLEDGE AND INFORMATION SYSTEMS 2024年第12期66卷 7419-7435页

作者： Ziaee, Seyed Sina Rahmani, Hossein Nazari, Mohammad Iran Univ Sci & Technol Sch Comp Engn Tehran *** Iran Univ Calgary Dept Comp Sci Calgary T2N 1N4 AB Canada

Nowadays, with the advent of movies and TV shows and the competition between different movie streamer companies and movie databases to attract more users, movie recommenders have become a major prerequisite for customer satisfaction. Most of the previously introduced methods used collaborative, content-based, and hybrid filtering techniques, where neural network-based approaches and matrix completion are the major approaches of most recent movie recommender systems. The major drawbacks of previous systems are not considering side information, such as plot synopsis and cold start problem, in the context of movie recommendations. In this paper, we propose a novel inductive approach called MoRGH which first constructs a graph of similar movies by considering the information available in movies' plot synopsis and genres. Second, we construct a heterogeneous graph that includes two types of nodes: movies and users. This graph is built using the MovieLens dataset and the similarity graph generated in the first stage, where each edge between a user and a movie represents the user's rating for that movie, and each edge between two movies represents the similarity between them. Third, MoRGH mitigates the drawbacks of previous methods by employing a GNN and GAE-based model that combines collaborative and content-based approaches. This hybrid approach allows MoRGH to provide accurate and more personalized recommendations for each user, outperforming previous state-of-the-art models in terms of RMSE scores. The achieved improvement in RMSE scores demonstrates MoRGH's superior performance and its ability to deliver enhanced recommendations compared to existing models.

关键词： Movie recommender system graph neural network graph autoencoder graph inductive learning

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Drug repositioning based on residual attention network and free multiscale adversarial training

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BMC BIOINFORMATICS 2024年第1期25卷 1页

作者： Li, Guanghui Li, Shuwen Liang, Cheng Xiao, Qiu Luo, Jiawei East China Jiaotong Univ Sch Informat Engn Nanchang Peoples R China Shandong Normal Univ Sch Informat Sci & Engn Jinan Peoples R China Hunan Normal Univ Coll Informat Sci & Engn Changsha Peoples R China Hunan Univ Coll Comp Sci & Elect Engn Changsha Peoples R China

BackgroundConducting traditional wet experiments to guide drug development is an expensive, time-consuming and risky process. Analyzing drug function and repositioning plays a key role in identifying new therapeutic potential of approved drugs and discovering therapeutic approaches for untreated diseases. Exploring drug-disease associations has far-reaching implications for identifying disease pathogenesis and treatment. However, reliable detection of drug-disease relationships via traditional methods is costly and slow. Therefore, investigations into computational methods for predicting drug-disease associations are currently *** paper presents a novel drug-disease association prediction method, RAFGAE. First, RAFGAE integrates known associations between diseases and drugs into a bipartite network. Second, RAFGAE designs the Re_GAT framework, which includes multilayer graph attention networks (GATs) and two residual networks. The multilayer GATs are utilized for learning the node embeddings, which is achieved by aggregating information from multihop neighbors. The two residual networks are used to alleviate the deep network oversmoothing problem, and an attention mechanism is introduced to combine the node embeddings from different attention layers. Third, two graph autoencoders (GAEs) with collaborative training are constructed to simulate label propagation to predict potential associations. On this basis, free multiscale adversarial training (FMAT) is introduced. FMAT enhances node feature quality through small gradient adversarial perturbation iterations, improving the prediction performance. Finally, tenfold cross-validations on two benchmark datasets show that RAFGAE outperforms current methods. In addition, case studies have confirmed that RAFGAE can detect novel drug-disease *** comprehensive experimental results validate the utility and accuracy of RAFGAE. We believe that this method may serve as an excellent predictor

关键词： graph attention network Residual network graph autoencoder Adversarial training Drug-disease association

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