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A Survey of Key Technologies for Multi-source Heterogeneous Data in Intelligent Manufacturing 认领 引用
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作者 Minghao Zhu Pengfei Yang +2 位作者 Bo Gao Xuehan Li Letian Wang 《Instrumentation》 2026年第1期26-39,共14页
Currently,most enterprises have adopted information software and digital equipment and gradually established digital factories.They conduct enterprise data collection and decision-support activities,generating large v... Currently,most enterprises have adopted information software and digital equipment and gradually established digital factories.They conduct enterprise data collection and decision-support activities,generating large volumes of multi-source heterogeneous data across all stages of the product life cycle.However,current data utilization methods remain simplistic,and the goal of leveraging multi-source heterogeneous data to drive manufacturing value has yet to be fully realized.To address this issue,this study first defines the concept and characteristics of multi-source heterogeneous data in intelligent manufacturing,based on an analysis of its relationship with industrial big data.Then,integrating principles from data science,a technological framework for multi-source heterogeneous data is proposed.The key technologies involved in each stage of data processing are investigated,and typical applications of such data in intelligent manufacturing are discussed.Finally,this paper analyzes the challenges and future development directions of multi-source heterogeneous data processing in intelligent manufacturing.The goal is to provide theoretical and technical support for integrating intelligent manufacturing with data science. 展开更多
关键词 intelligent manufacturing multi-source heterogeneous data feature fusion data system technological framework
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Distribution Network Partitioning for Voltage Regulation Using Heterogeneous Graph Neural Networks Considering Cyber-Attacks Risk 认领 引用
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作者 Lei Xu Bo Zhang +1 位作者 Chunxia Dou Dong Yue 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2026年第4期995-997,共3页
Dear Editor,The integration of distributed energy resources(DERs)and communication infrastructures makes distribution networks increasingly cyber-physical,requiring resilient and real-time voltage regulation.Network p... Dear Editor,The integration of distributed energy resources(DERs)and communication infrastructures makes distribution networks increasingly cyber-physical,requiring resilient and real-time voltage regulation.Network partitioning enables scalable control,yet existing methods often ignore communication and security constraints or rely on costly optimization,limiting practicality under dynamic and adversarial conditions. 展开更多
关键词 scalable controlyet distribution network partitioning heterogeneous graph neural networks distributed energy resources communication infrastructures cyber attacks risk distributed energy resources ders voltage regulation
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Large Language Model-Based Representations of Heterogeneous Graphs for Vulnerability Detection 认领 引用
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作者 Xiaorong Feng Ying Gao Leyu Shi 《Computers, Materials & Continua》 SCIE EI 2026年第9期540-569,共30页
Open source software has become a fundamental component of modern software ecosystems,supporting a wide range of critical applications in operating systems,cloud services,embedded systems,and security-sensitive infras... Open source software has become a fundamental component of modern software ecosystems,supporting a wide range of critical applications in operating systems,cloud services,embedded systems,and security-sensitive infrastructures.However,the rapid growth of open source projects also brings increasingly serious security challenges.Many widely used C/C++components still contain hidden vulnerabilities,and attackers are no longer limited to exploiting traditional memory-related bugs such as buffer overflows or use-after-free errors.In recent years,non-memory logic flaws,including improper authentication,incorrect state transitions,flawed boundary checks,and insecure API usage,have become more prevalent and more difficult to detect using conventional static analysis or pattern-matching methods.To address these limitations,this study proposes a novel vulnerability detection framework that combines the semantic understanding capability of large language models(LLMs)with the structural representation ability of heterogeneous graph learning.Specifically,we construct a Heterogeneous Vulnerability Graph(HeVG)to explicitly model multiple types of code structures in C/C++programs,including syntax,control flow,data dependency,and function-call relationships.By representing source code as a heterogeneous graph,the proposed framework can capture both local code patterns and long-range dependencies that are essential for identifying complex vulnerabilities.In addition,a cross-modal alignment mechanism is introduced to effectively fuse code-text semantic features extracted by LLMs with graph-based structural representations.This enables the model to jointly understand what the code means and how different program elements interact.Experimental results show that the proposed approach achieves state-of-the-art performance,reaching 80.24%accuracy in single-file vulnerability detection and 71.78%accuracy in cross-file vulnerability detection.Further analysis demonstrates that the framework is particularly effective in detecting complex logic vulnerabilities and maintains strong generalization ability across different projects.These results indicate that integrating LLMs with heterogeneous graph learning provides a promising direction for more accurate and robust open source software vulnerability detection.Open source software faces growing security challenges,with widespread vulnerabilities in critical components and an increasing prevalence of non-memory logic flaws.To address these issues,this study proposes a novel vulnerability detection framework that integrates large language models(LLMs)with heterogeneous graph learning.We introduce a Heterogeneous Vulnerability Graph(HeVG)to explicitly model diverse code structures in C/C++programs,and employ a cross-modal alignment mechanism to fuse semantic information from code text with graph representations.Experimental results demonstrate the effectiveness of our approach,achieving state-of-the-art performance in both single-file(80.24%accuracy)and cross-file(71.78%accuracy)vulnerability detection.The framework shows particular strength in identifying complex logic vulnerabilities while maintaining high generalization capability across projects. 展开更多
关键词 Large language model graph neural network open source software security vulnerability detection heterogeneous vulnerability graph
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New Method of Multi-Source Heterogeneous Data Signal Processing of Power Internet of Things Based on Compressive Sensing 认领 引用
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作者 Li Yongjie Shen Jing +3 位作者 Zang Huaping Hou Huanpeng Yang Yimu Yao Haoyu 《China Communications》 SCIE EI CSCD 2025年第11期242-255,共14页
In the heterogeneous power internet of things(IoT)environment,data signals are acquired to support different business systems to realize advanced intelligent applications,with massive,multi-source,heterogeneous and ot... In the heterogeneous power internet of things(IoT)environment,data signals are acquired to support different business systems to realize advanced intelligent applications,with massive,multi-source,heterogeneous and other characteristics.Reliable perception of information and efficient transmission of energy in multi-source heterogeneous environments are crucial issues.Compressive sensing(CS),as an effective method of signal compression and transmission,can accurately recover the original signal only by very few sampling.In this paper,we study a new method of multi-source heterogeneous data signal reconstruction of power IoT based on compressive sensing technology.Based on the traditional compressive sensing technology to directly recover multi-source heterogeneous signals,we fully use the interference subspace information to design the measurement matrix,which directly and effectively eliminates the interference while making the measurement.The measure matrix is optimized by minimizing the average cross-coherence of the matrix,and the reconstruction performance of the new method is further improved.Finally,the effectiveness of the new method with different parameter settings under different multi-source heterogeneous data signal cases is verified by using orthogonal matching pursuit(OMP)and sparsity adaptive matching pursuit(SAMP)for considering the actual environment with prior information utilization of signal sparsity and no prior information utilization of signal sparsity. 展开更多
关键词 compressive sensing heterogeneous power internet of things multi-source heterogeneous signal reconstruction
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MMH-FE:AMulti-Precision and Multi-Sourced Heterogeneous Privacy-Preserving Neural Network Training Based on Functional Encryption 认领 引用
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作者 Hao Li Kuan Shao +2 位作者 Xin Wang Mufeng Wang Zhenyong Zhang 《Computers, Materials & Continua》 SCIE EI 2025年第3期5387-5405,共19页
Due to the development of cloud computing and machine learning,users can upload their data to the cloud for machine learning model training.However,dishonest clouds may infer user data,resulting in user data leakage.P... Due to the development of cloud computing and machine learning,users can upload their data to the cloud for machine learning model training.However,dishonest clouds may infer user data,resulting in user data leakage.Previous schemes have achieved secure outsourced computing,but they suffer from low computational accuracy,difficult-to-handle heterogeneous distribution of data from multiple sources,and high computational cost,which result in extremely poor user experience and expensive cloud computing costs.To address the above problems,we propose amulti-precision,multi-sourced,andmulti-key outsourcing neural network training scheme.Firstly,we design a multi-precision functional encryption computation based on Euclidean division.Second,we design the outsourcing model training algorithm based on a multi-precision functional encryption with multi-sourced heterogeneity.Finally,we conduct experiments on three datasets.The results indicate that our framework achieves an accuracy improvement of 6%to 30%.Additionally,it offers a memory space optimization of 1.0×224 times compared to the previous best approach. 展开更多
关键词 Functional encryption multi-sourced heterogeneous data privacy preservation neural networks
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Multi-Source Heterogeneous Data Fusion Analysis Platform for Thermal Power Plants 认领 引用
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作者 Jianqiu Wang Jianting Wen +1 位作者 Hui Gao Chenchen Kang 《Journal of Architectural Research and Development》 2025年第6期24-28,共5页
With the acceleration of intelligent transformation of energy system,the monitoring of equipment operation status and optimization of production process in thermal power plants face the challenge of multi-source heter... With the acceleration of intelligent transformation of energy system,the monitoring of equipment operation status and optimization of production process in thermal power plants face the challenge of multi-source heterogeneous data integration.In view of the heterogeneous characteristics of physical sensor data,including temperature,vibration and pressure that generated by boilers,steam turbines and other key equipment and real-time working condition data of SCADA system,this paper proposes a multi-source heterogeneous data fusion and analysis platform for thermal power plants based on edge computing and deep learning.By constructing a multi-level fusion architecture,the platform adopts dynamic weight allocation strategy and 5D digital twin model to realize the collaborative analysis of physical sensor data,simulation calculation results and expert knowledge.The data fusion module combines Kalman filter,wavelet transform and Bayesian estimation method to solve the problem of data time series alignment and dimension difference.Simulation results show that the data fusion accuracy can be improved to more than 98%,and the calculation delay can be controlled within 500 ms.The data analysis module integrates Dymola simulation model and AERMOD pollutant diffusion model,supports the cascade analysis of boiler combustion efficiency prediction and flue gas emission monitoring,system response time is less than 2 seconds,and data consistency verification accuracy reaches 99.5%. 展开更多
关键词 Thermal power plant Multi-source heterogeneous data Data fusion analysis platform Edge computing
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Multi-source heterogeneous data access management framework and key technologies for electric power Internet of Things 认领 引用 被引量：3
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作者 Pengtian Guo Kai Xiao +1 位作者 Xiaohui Wang Daoxing Li 《Global Energy Interconnection》 EI CSCD 2024年第1期94-105,共12页
The power Internet of Things(IoT)is a significant trend in technology and a requirement for national strategic development.With the deepening digital transformation of the power grid,China’s power system has initiall... The power Internet of Things(IoT)is a significant trend in technology and a requirement for national strategic development.With the deepening digital transformation of the power grid,China’s power system has initially built a power IoT architecture comprising a perception,network,and platform application layer.However,owing to the structural complexity of the power system,the construction of the power IoT continues to face problems such as complex access management of massive heterogeneous equipment,diverse IoT protocol access methods,high concurrency of network communications,and weak data security protection.To address these issues,this study optimizes the existing architecture of the power IoT and designs an integrated management framework for the access of multi-source heterogeneous data in the power IoT,comprising cloud,pipe,edge,and terminal parts.It further reviews and analyzes the key technologies involved in the power IoT,such as the unified management of the physical model,high concurrent access,multi-protocol access,multi-source heterogeneous data storage management,and data security control,to provide a more flexible,efficient,secure,and easy-to-use solution for multi-source heterogeneous data access in the power IoT. 展开更多
关键词 Power Internet of Things Object model High concurrency access Zero trust mechanism Multi-source heterogeneous data
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DHGT-DTI:Advancing drug-target interaction prediction through a dual-view heterogeneous network with GraphSAGE and Graph Transformer 认领 引用
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作者 Mengdi Wang Xiujuan Lei +2 位作者 Ling Guo Ming Chen Yi Pan 《Journal of Pharmaceutical Analysis》 SCIE CAS CSCD 2025年第10期2442-2456,共15页
Computational approaches for predicting drug-target interactions(DTIs)are pivotal in advancing drug discovery.Current methodologies leveraging heterogeneous networks often fall short in fully integrating both local an... Computational approaches for predicting drug-target interactions(DTIs)are pivotal in advancing drug discovery.Current methodologies leveraging heterogeneous networks often fall short in fully integrating both local and global network information.To comprehensively consider network information,we propose DHGT-DTI,a novel deep learning-based approach for DTI prediction.Specifically,we capture the local and global structural information of the network from both neighborhood and meta-path per-spectives.In the neighborhood perspective,we employ a heterogeneous graph neural network(HGNN),which extends Graph Sample and Aggregate(GraphSAGE)to handle diverse node and edge types,effectively learning local network structures.In the meta-path perspective,we introduce a Graph Transformer with residual connections to model higher-order relationships defined by meta-paths,such as"drug-disease-drug",and use an attention mechanism to fuse information across multiple meta-paths.The learned features from these dual perspectives are synergistically integrated for DTI prediction via a matrix decomposition method.Furthermore,DHGT-DTI reconstructs not only the DTI network but also auxiliary networks to bolster prediction accuracy.Comprehensive experiments on two benchmark datasets validate the superiority of DHGT-DTI over existing baseline methods.Additionally,case studies on six drugs used to treat Parkinson's disease not only validate the practical utility of DHGT-DTI but also highlight its broader potential in accelerating drug discovery for other diseases. 展开更多
关键词 Drug-target interaction(DTI) Graph Transformer Graph sample and aggregate(GraphSAGE) Heterogeneous network
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Knowledge graphs in heterogeneous catalysis: Recent advances and future opportunities 认领 引用
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作者 Raúl Díaz Hongliang Xin 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2025年第8期179-189,共11页
Knowledge graphs (KGs) offer a structured, machine-readable format for organizing complex information. In heterogeneous catalysis, where data on catalytic materials, reaction conditions, mechanisms, and synthesis rout... Knowledge graphs (KGs) offer a structured, machine-readable format for organizing complex information. In heterogeneous catalysis, where data on catalytic materials, reaction conditions, mechanisms, and synthesis routes are dispersed across diverse sources, KGs provide a semantic framework that supports data integration under the FAIR (Findable, Accessible, Interoperable, and Reusable) principles. This review aims to survey recent developments in catalysis KGs, describe the main techniques for graph construction, and highlight how artificial intelligence, particularly large language models (LLMs), enhances graph generation and query. We conducted a systematic analysis of the literature, focusing on ontology-guided text mining pipelines, graph population methods, and maintenance strategies. Our review identifies key trends: ontology-based approaches enable the automated extraction of domain knowledge, LLM-driven retrieval-augmented generation supports natural-language queries, and scalable graph architectures range from a few thousand to over a million triples. We discuss state-of-the-art applications, such as catalyst recommendation systems and reaction mechanism discovery tools, and examine the major challenges, including data heterogeneity, ontology alignment, and long-term graph curation. We conclude that KGs, when combined with AI methods, hold significant promise for accelerating catalyst discovery and knowledge management, but progress depends on establishing community standards for ontology development and maintenance. This review provides a roadmap for researchers seeking to leverage KGs to advance heterogeneous catalysis research. 展开更多
关键词 Heterogeneous catalysis Knowledge graph Ontology Large language models Deep learning
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Output Consensus of Heterogeneous Linear MASs via Adaptive Event-Triggered Feedback Combination Control 认领 引用
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作者 Shuo Yuan Chengpu Yu Jian Sun 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2025年第1期285-287,共3页
Dear Editor,This letter studies output consensus problem of heterogeneous linear multiagent systems over directed graphs. A novel adaptive dynamic event-triggered controller is presented based only on the feedback com... Dear Editor,This letter studies output consensus problem of heterogeneous linear multiagent systems over directed graphs. A novel adaptive dynamic event-triggered controller is presented based only on the feedback combination of the agent's own state and neighbors' output,which can achieve exponential output consensus through intermittent communication. The controller is obtained by solving two linear matrix equations, and Zeno behavior is excluded. 展开更多
关键词 intermittent communication feedback combination heterogeneous linear multiagent systems exponential output consensus directed graphs output consensus problem output consensus solving two linear matrix equations
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Attack Behavior Extraction Based on Heterogeneous Cyberthreat Intelligence and Graph Convolutional Networks 认领 引用 被引量：2
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作者 Binhui Tang Junfeng Wang +3 位作者 Huanran Qiu Jian Yu Zhongkun Yu Shijia Liu 《Computers, Materials & Continua》 SCIE EI 2023年第1期235-252,共18页
The continuous improvement of the cyber threat intelligence sharing mechanism provides new ideas to deal with Advanced Persistent Threats(APT).Extracting attack behaviors,i.e.,Tactics,Techniques,Procedures(TTP)from Cy... The continuous improvement of the cyber threat intelligence sharing mechanism provides new ideas to deal with Advanced Persistent Threats(APT).Extracting attack behaviors,i.e.,Tactics,Techniques,Procedures(TTP)from Cyber Threat Intelligence(CTI)can facilitate APT actors’profiling for an immediate response.However,it is difficult for traditional manual methods to analyze attack behaviors from cyber threat intelligence due to its heterogeneous nature.Based on the Adversarial Tactics,Techniques and Common Knowledge(ATT&CK)of threat behavior description,this paper proposes a threat behavioral knowledge extraction framework that integrates Heterogeneous Text Network(HTN)and Graph Convolutional Network(GCN)to solve this issue.It leverages the hierarchical correlation relationships of attack techniques and tactics in the ATT&CK to construct a text network of heterogeneous cyber threat intelligence.With the help of the Bidirectional EncoderRepresentation fromTransformers(BERT)pretraining model to analyze the contextual semantics of cyber threat intelligence,the task of threat behavior identification is transformed into a text classification task,which automatically extracts attack behavior in CTI,then identifies the malware and advanced threat actors.The experimental results show that F1 achieve 94.86%and 92.15%for the multi-label classification tasks of tactics and techniques.Extend the experiment to verify the method’s effectiveness in identifying the malware and threat actors in APT attacks.The F1 for malware and advanced threat actors identification task reached 98.45%and 99.48%,which are better than the benchmark model in the experiment and achieve state of the art.The model can effectivelymodel threat intelligence text data and acquire knowledge and experience migration by correlating implied features with a priori knowledge to compensate for insufficient sample data and improve the classification performance and recognition ability of threat behavior in text. 展开更多
关键词 Attack behavior extraction cyber threat intelligence(CTI) graph convolutional network(GCN) heterogeneous textual network(HTN)
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A novel modeling approach for vertical handover based on dynamic k-partite graph in heterogeneous networks 认领 引用 被引量：4
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作者 Mohamed Lahby Ayoub Essouiri Abderrahim Sekkaki 《Digital Communications and Networks》 SCIE 2019年第4期297-307,共11页
The future network world will be embedded with different generations of wireless technologies,such as 3G,4G and 5G.At the same time,the development of new devices equipped with multiple interfaces is growing rapidly i... The future network world will be embedded with different generations of wireless technologies,such as 3G,4G and 5G.At the same time,the development of new devices equipped with multiple interfaces is growing rapidly in recent years.As a consequence,the vertical handover protocol is developed in order to provide ubiquitous connectivity in the heterogeneous wireless environment.Indeed,by using this protocol,the users have opportunities to be connected to the Internet through a variety of wireless technologies at any time and anywhere.The main challenge of this protocol is how to select the best access network in terms of Quality of Service(QoS)for users.For that,many algorithms have been proposed and developed to deal with the issue in recent studies.However,all existing algorithms permit only the selection of one access network from the available networks during the vertical handover process.To cope with this problem,in this paper we propose a new approach based on k-partite graph.Firstly,we introduce k-partite graph theory to model the vertical handover problem.Secondly,the selection of the best path is performed by a robust and lightweight mechanism based on cost function and Dijkstra’s algorithm.The experimental results show that the proposed approach can achieve better performance of QoS than the existing algorithms for FTP traffic and video streaming. 展开更多
关键词 Heterogeneous wireless networks Vertical handover K-partite graph Cost function QoS Mininet
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A Software Defect Prediction Method Using a Multivariate Heterogeneous Hybrid Deep Learning Algorithm 认领 引用
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作者 Qi Fei Haojun Hu +1 位作者 Guisheng Yin Zhian Sun 《Computers, Materials & Continua》 SCIE EI 2025年第2期3251-3279,共29页
Software defect prediction plays a critical role in software development and quality assurance processes. Effective defect prediction enables testers to accurately prioritize testing efforts and enhance defect detecti... Software defect prediction plays a critical role in software development and quality assurance processes. Effective defect prediction enables testers to accurately prioritize testing efforts and enhance defect detection efficiency. Additionally, this technology provides developers with a means to quickly identify errors, thereby improving software robustness and overall quality. However, current research in software defect prediction often faces challenges, such as relying on a single data source or failing to adequately account for the characteristics of multiple coexisting data sources. This approach may overlook the differences and potential value of various data sources, affecting the accuracy and generalization performance of prediction results. To address this issue, this study proposes a multivariate heterogeneous hybrid deep learning algorithm for defect prediction (DP-MHHDL). Initially, Abstract Syntax Tree (AST), Code Dependency Network (CDN), and code static quality metrics are extracted from source code files and used as inputs to ensure data diversity. Subsequently, for the three types of heterogeneous data, the study employs a graph convolutional network optimization model based on adjacency and spatial topologies, a Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) hybrid neural network model, and a TabNet model to extract data features. These features are then concatenated and processed through a fully connected neural network for defect prediction. Finally, the proposed framework is evaluated using ten promise defect repository projects, and performance is assessed with three metrics: F1, Area under the curve (AUC), and Matthews correlation coefficient (MCC). The experimental results demonstrate that the proposed algorithm outperforms existing methods, offering a novel solution for software defect prediction. 展开更多
关键词 Software defect prediction multiple heterogeneous data graph convolutional network models based on adjacency and spatial topologies CNN-BiLSTM TabNet
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Heterogeneous graph construction and node representation learning method of Treatise on Febrile Diseases based on graph convolutional network 认领 引用 被引量：2
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作者 YAN Junfeng WEN Zhihua ZOU Beiji 《Digital Chinese Medicine》 CAS 2022年第4期419-428,共10页
Objective To construct symptom-formula-herb heterogeneous graphs structured Treatise on Febrile Diseases(Shang Han Lun,《伤寒论》)dataset and explore an optimal learning method represented with node attributes based o... Objective To construct symptom-formula-herb heterogeneous graphs structured Treatise on Febrile Diseases(Shang Han Lun,《伤寒论》)dataset and explore an optimal learning method represented with node attributes based on graph convolutional network(GCN).Methods Clauses that contain symptoms,formulas,and herbs were abstracted from Treatise on Febrile Diseases to construct symptom-formula-herb heterogeneous graphs,which were used to propose a node representation learning method based on GCN−the Traditional Chinese Medicine Graph Convolution Network(TCM-GCN).The symptom-formula,symptom-herb,and formula-herb heterogeneous graphs were processed with the TCM-GCN to realize high-order propagating message passing and neighbor aggregation to obtain new node representation attributes,and thus acquiring the nodes’sum-aggregations of symptoms,formulas,and herbs to lay a foundation for the downstream tasks of the prediction models.Results Comparisons among the node representations with multi-hot encoding,non-fusion encoding,and fusion encoding showed that the Precision@10,Recall@10,and F1-score@10 of the fusion encoding were 9.77%,6.65%,and 8.30%,respectively,higher than those of the non-fusion encoding in the prediction studies of the model.Conclusion Node representations by fusion encoding achieved comparatively ideal results,indicating the TCM-GCN is effective in realizing node-level representations of heterogeneous graph structured Treatise on Febrile Diseases dataset and is able to elevate the performance of the downstream tasks of the diagnosis model. 展开更多
关键词 Graph convolutional network(GCN) Heterogeneous graph Treatise on Febrile Diseases(Shang Han Lun,《伤寒论》) Node representations on heterogeneous graph Node representation learning
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Insider threat detection approach for tobacco industry based on heterogeneous graph embedding 认领 引用
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作者 季琦 LI Wei +2 位作者 PAN Bailin XUE Hongkai QIU Xiang 《High Technology Letters》 EI CAS 2024年第2期199-210,共12页
In the tobacco industry,insider employee attack is a thorny problem that is difficult to detect.To solve this issue,this paper proposes an insider threat detection method based on heterogeneous graph embedding.First,t... In the tobacco industry,insider employee attack is a thorny problem that is difficult to detect.To solve this issue,this paper proposes an insider threat detection method based on heterogeneous graph embedding.First,the interrelationships between logs are fully considered,and log entries are converted into heterogeneous graphs based on these relationships.Second,the heterogeneous graph embedding is adopted and each log entry is represented as a low-dimensional feature vector.Then,normal logs and malicious logs are classified into different clusters by clustering algorithm to identify malicious logs.Finally,the effectiveness and superiority of the method is verified through experiments on the CERT dataset.The experimental results show that this method has better performance compared to some baseline methods. 展开更多
关键词 insider threat detection advanced persistent threats graph construction heterogeneous graph embedding
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Topic-Aware Abstractive Summarization Based on Heterogeneous Graph Attention Networks for Chinese Complaint Reports 认领 引用
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作者 Yan Li Xiaoguang Zhang +4 位作者 Tianyu Gong Qi Dong Hailong Zhu Tianqiang Zhang Yanji Jiang 《Computers, Materials & Continua》 SCIE EI 2023年第9期3691-3705,共15页
Automatic text summarization(ATS)plays a significant role in Natural Language Processing(NLP).Abstractive summarization produces summaries by identifying and compressing the most important information in a document.Ho... Automatic text summarization(ATS)plays a significant role in Natural Language Processing(NLP).Abstractive summarization produces summaries by identifying and compressing the most important information in a document.However,there are only relatively several comprehensively evaluated abstractive summarization models that work well for specific types of reports due to their unstructured and oral language text characteristics.In particular,Chinese complaint reports,generated by urban complainers and collected by government employees,describe existing resident problems in daily life.Meanwhile,the reflected problems are required to respond speedily.Therefore,automatic summarization tasks for these reports have been developed.However,similar to traditional summarization models,the generated summaries still exist problems of informativeness and conciseness.To address these issues and generate suitably informative and less redundant summaries,a topic-based abstractive summarization method is proposed to obtain global and local features.Additionally,a heterogeneous graph of the original document is constructed using word-level and topic-level features.Experiments and analyses on public review datasets(Yelp and Amazon)and our constructed dataset(Chinese complaint reports)show that the proposed framework effectively improves the performance of the abstractive summarization model for Chinese complaint reports. 展开更多
关键词 Text summarization topic Chinese complaint report heterogeneous graph attention network
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Event Relation Extraction Based on Heterogeneous Graph Attention Networks and Event Ontology Direction Induction 认领 引用 被引量：1
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作者 Wenjie Liu Zhifan Wang 《Tsinghua Science and Technology》 SCIE EI CAS CSCD 2026年第1期504-517,共14页
Event relation extraction plays a crucial role in constructing an event knowledge graph.However,current models only extract trigger words as event ontology representations,and do not consider node type during informat... Event relation extraction plays a crucial role in constructing an event knowledge graph.However,current models only extract trigger words as event ontology representations,and do not consider node type during information aggregation,resulting in low accuracy in event relation extraction.To address these challenges,we propose an event relation extraction model based on heterogeneous graph attention networks and event ontology direction induction.To enhance the completeness of event information,we incorporate argument role information,in addition to trigger words,into the input text.A novel heterogeneous graph attention framework is proposed to reasonably allocate weights to trigger words,argument roles,and text information,and then perform two levels of aggregation,node-level and semantic-level,in sequence.To improve the accuracy of event direction discrimination,we construct an event ontology subgraph that includes trigger words and arguments to aggregate complete event structure information during direction induction.Finally,we evaluate our model on three datasets,TimeBank-Dense,MATRES,and HiEve,and demonstrate that our model outperforms state-of-the-art models by 1.2%,0.5%,and 0.8%,respectively,in terms of the Micro-F1 score.Our proposed model provides a promising solution for event relation extraction and can be applied in various natural language processing applications. 展开更多
关键词 event relation extraction argument role heterogeneous graph networks Event Ontology Direction Induction(EODI)
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Missing data recovery for heterogeneous graphs with incremental multi-source data fusion 认领 引用
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作者 Yang LIU Xiaoxia JIANG +2 位作者 Yuanning CUI Yu WANG Wei HU 《Frontiers of Computer Science》 SCIE EI CSCD 2025年第12期163-177,共15页
Heterogeneous graphs organize data with nodes and edges,and have been widely used in various graph-centric applications.Often,some data are omitted during manual construction,leading to data reduction and performance ... Heterogeneous graphs organize data with nodes and edges,and have been widely used in various graph-centric applications.Often,some data are omitted during manual construction,leading to data reduction and performance degeneration on downstream tasks.Existing methods recover the missing data based on the data already within a single graph,neglecting the fact that graphs from different sources share some common nodes due to scope overlap.In this paper,we concentrate on the missing data recovery task on multi-source heterogeneous graphs under the incremental scenario and design a novel framework to recover the missing data by fusing multi-source complementary data from previously appeared graphs.Our model,namely SIKE,is present with a pre-trained language model and graph-specific adapters.To take advantage of the complementary data of multi-source graphs,we propose an embedding-based data fusion method to gather data among graphs.To evaluate the proposed model,we build two new datasets consisting of multi-source heterogeneous graphs.The experimental results show that our model SIKE achieves significant improvements compared with competitive baseline models,demonstrating the effectiveness of our model and shedding light on multi-source data fusion for data governance. 展开更多
关键词 data governance missing data recovery heterogeneous graph language model
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A New Meta-path Monte Carlo Tree Search Algorithm for Heterogeneous Graph Neural Networks 认领 引用
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作者 Bin Zhang Zhipeng Cheng +3 位作者 Chenglong Du Zhengganzhe Chen Chaoyang Chen Weihua Gui 《Machine Intelligence Research》 EI CSCD 2026年第3期647-661,共15页
Heterogeneous graph neural networks(HGNN)can capture heterogeneous semantic information in heterogeneous networks,learn the low-dimensional embedding vectors,and use them for downstream tasks.The selection of meta-pat... Heterogeneous graph neural networks(HGNN)can capture heterogeneous semantic information in heterogeneous networks,learn the low-dimensional embedding vectors,and use them for downstream tasks.The selection of meta-paths is always the focus of HGNN.Existing HGNN models often employ random selections of meta-paths or utilize all meta-paths with a fixed maximum number of hops,thereby overlooking significant heterogeneous semantic information of graphs and struggling to effectively leverage non-redundant information.To this end,a new Monte Carlo tree search-based heterogeneous graph neural network(MCTS-HGNN)model is developed to search for the appropriate set of meta-paths in heterogeneous graphs automatically,thus overcoming the difficulty of meta-path selection.Subsequently,the meta-path set is decomposed based on aggregation objects and independently applied to a subset of meta-paths by using a customized transformer-based semantic aggregation module,and then the diverse semantic information from meta-paths can be effectively utilized.Furthermore,the information from the meta-path subset is integrated by the graph-level transformer to achieve a comprehensive heterogeneous graph embedding.The learned embedding is evaluated via the downstream task of the heterogeneous graph.Finally,the ablation experiments validate the effectiveness of the module designed for the MCTS-HGNN.The experimental results demonstrate that the MCTS-HGNN outperforms state-of-the-art baselines across all evaluation metrics. 展开更多
关键词 Graph neural network heterogeneous network meta-path search node embedding node classification
Heterogeneous Network Embedding: A Survey 认领 引用 被引量：1
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作者 Sufen Zhao Rong Peng +1 位作者 Po Hu Liansheng Tan 《Computer Modeling in Engineering & Sciences》 SCIE EI 2023年第10期83-130,共48页
Real-world complex networks are inherently heterogeneous;they have different types of nodes,attributes,and relationships.In recent years,various methods have been proposed to automatically learn how to encode the stru... Real-world complex networks are inherently heterogeneous;they have different types of nodes,attributes,and relationships.In recent years,various methods have been proposed to automatically learn how to encode the structural and semantic information contained in heterogeneous information networks(HINs)into low-dimensional embeddings;this task is called heterogeneous network embedding(HNE).Efficient HNE techniques can benefit various HIN-based machine learning tasks such as node classification,recommender systems,and information retrieval.Here,we provide a comprehensive survey of key advancements in the area of HNE.First,we define an encoder-decoder-based HNE model taxonomy.Then,we systematically overview,compare,and summarize various state-of-the-art HNE models and analyze the advantages and disadvantages of various model categories to identify more potentially competitive HNE frameworks.We also summarize the application fields,benchmark datasets,open source tools,andperformance evaluation in theHNEarea.Finally,wediscuss open issues and suggest promising future directions.We anticipate that this survey will provide deep insights into research in the field of HNE. 展开更多
关键词 Heterogeneous information networks representation learning heterogeneous network embedding graph neural networks machine learning
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