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Directed message passing neural network (D-MPNN) with graph edge attention (GEA) for property prediction of biofuel-relevant species

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能源与人工智能(英文) 2022年第4期10卷 115-125页

作者： Xu Han Ming Jia Yachao Chang Yaopeng Li Shaohua Wu Key Laboratory of Ocean Energy Utilization and Energy Conservation of Ministry of Education Dalian University of TechnologyDalian 116024China

Predictive models based on graph neural network (GNN) have attracted increasing interest in quantitative structure-property relation (QSPR) modeling of organic species including biofuel components in recent years. For the task of property prediction of biofuel-relevant species, the present work applies the Directed Message Passing neural network (D-MPNN) framework, an emerging type of GNN, and incorporates graph attention on the DMPNN architecture to improve its capability. modeling using other common machine learning methods is also conducted, confirming the advantage of D-MPNN in comparison. graph Edge Attention (GEA) is proposed for the D-MPNN layers and shows success in increasing model accuracy after implementation. A relatively sizable subset from the QM9 data and 4 other datasets forming a wide scope of target properties (e.g., thermodynamic properties, ignition properties, surface tension, etc.) are selected for the models. A breakdown analysis of the species distribution of these datasets is conducted for more informed modeling. As the data availability of biofuel species is often a main obstacle for related modeling tasks, this study shows that the performance of D-MPNN with the proposed GEA attention mechanism is most enhanced when using a medium data size of 2000~5000. Some discussions are made regarding data issues and the use of machine learning methods and graph attention for the predictive modeling of biofuel properties, pointing out the need for more data with better species distribution that is representative of biofuels.

关键词： Deep learning Biofuel Quantitative structure-property relation Machine learning Molecules graph neural network

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Meta-path reasoning of knowledge graph for commonsense question answering

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Frontiers of Computer Science 2024年第1期18卷 49-59页

作者： Miao ZHANG Tingting HE Ming DONG National Engineering Research Center for E-Learning Central China Normal UniversityWuhan 430079China Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning Central China Normal UniversityWuhan 430079China National Language Resources Monitoring&Research Center for network Media Central China Normal UniversityWuhan 430079China School of Computer Central China Normal UniversityWuhan 430079China

Commonsense question answering(CQA)requires understanding and reasoning over QA context and related commonsense knowledge,such as a structured Knowledge graph(KG).Existing studies combine language models and graph neural networks to model ***,traditional knowledge graph are mostly concept-based,ignoring direct path evidence necessary for accurate *** this paper,we propose MRGNN(Meta-path Reasoning graph neural network),a novel model that comprehensively captures sequential semantic information from concepts and *** MRGNN,meta-paths are introduced as direct inference evidence and an original graph neural network is adopted to aggregate features from both concepts and paths *** conduct sufficient experiments on the CommonsenceQA and OpenBookQA datasets,showing the effectiveness of ***,we conduct further ablation experiments and explain the reasoning behavior through the case study.

关键词： question answering knowledge graph graph neural network meta-path reasoning

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Motion Planning for Autonomous Driving with Real Traffic Data Validation

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Chinese Journal of Mechanical Engineering 2024年第1期37卷 74-86页

作者： Wenbo Chu Kai Yang Shen Li Xiaolin Tang Western China Science City Innovation Center of Intelligent and Connected Vehicle Chongqing 400044China College of Mechanical and Vehicle Engineering Chongqing UniversityChongqing 400044China School of Civil Engineering Tsinghua UniversityBeijing 100084China

Accurate trajectory prediction of surrounding road users is the fundamental input for motion planning,which enables safe autonomous driving on public *** this paper,a safe motion planning approach is proposed based on the deep learning-based trajectory prediction *** begin with,a trajectory prediction model is established based on the graph neural network(GNN)that is trained utilizing the INTERACTION ***,the validated trajectory prediction model is used to predict the future trajectories of surrounding road users,including pedestrians and *** addition,a GNN prediction model-enabled motion planner is developed based on the model predictive control ***,two driving scenarios are extracted from the INTERACTION dataset to validate and evaluate the effectiveness of the proposed motion planning approach,i.e.,merging and roundabout *** results demonstrate that the proposed method can lower the risk and improve driving safety compared with the baseline method.

关键词： Trajectory prediction graph neural network Motion planning INTERACTION dataset

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Digital twin system framework and information model for industry chain based on industrial Internet

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Frontiers of Information Technology & Electronic Engineering 2024年第7期25卷 951-967页

作者： Wenxuan WANG Yongqin LIU Xudong CHAI Lin ZHANG School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100191China Department of Control Science and Engineering Jilin UniversityChangchun 130012China CASICloud China Aerospace Science and Industry Corporation LimitedBeijing 100080China

The integration of industrial Internet,cloud computing,and big data technology is changing the business and management mode of the industry ***,the industry chain is characterized by a wide range of fields,complex environment,and many factors,which creates a challenge for efficient integration and leveraging of industrial big *** at the integration of physical space and virtual space of the current industry chain,we propose an industry chain digital twin(DT)system framework for the industrial *** addition,an industry chain information model based on a knowledge graph(KG)is proposed to integrate complex and heterogeneous industry chain data and extract industrial ***,the ontology of the industry chain is established,and an entity alignment method based on scientific and technological achievements is ***,the bidirectional encoder representations from Transformers(BERT)based multi-head selection model is proposed for joint entity–relation extraction of industry chain ***,a relation completion model based on a relational graph convolutional network(R-GCN)and a graph sample and aggregate network(graphSAGE)is proposed which considers both semantic information and graph structure information of *** results show that the performances of the proposed joint entity–relation extraction model and relation completion model are significantly better than those of the ***,an industry chain information model is established based on the data of 18 industry chains in the field of basic machinery,which proves the feasibility of the proposed method.

关键词： Industry chain Digital twin Industrial Internet Knowledge graph graph neural network

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graphSTGAN:Situation understanding network of slow-fast high maneuvering targets for maritime monitor services of IoT data

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Digital Communications and networks 2024年第3期10卷 620-630页

作者： Guanlin Wu Haipeng Wang Yu Liu You He Shenzhen International Graduate School Tsinghua UniversityShenzhen518071China Institute of Space Science and Applied Technology Harbin Institute of TechnologyShenzhen518055China Department of Electronic Engineering Tsinghua UniversityBeijing100084China

With the rapid growth of the maritime Internet of Things(IoT)devices for Maritime Monitor Services(MMS),maritime traffic controllers could not handle a massive amount of data in *** unmanned MMS,one of the key technologies is situation ***,the presence of slow-fast high maneuvering targets and track breakages due to radar blind zones make modeling the dynamics of marine multi-agents difficult,and pose significant challenges to maritime situation *** order to comprehend the situation accurately and thus offer unmanned MMS,it is crucial to model the complex dynamics of multi-agents using IoT big ***,previous methods typically rely on complex assumptions,are plagued by unstructured data,and disregard the interactions between multiple agents and the spatial-temporal correlations.A deep learning model,graph Spatial-Temporal Generative Adversarial network(graphSTGAN),is proposed in this paper,which uses graph neural network to model unstructured data and uses STGAN to learn the spatial-temporal dependencies and *** experiments show the effectiveness and robustness of the proposed method.

关键词： Internet of things Multi-agents graph neural network Maritime monitoring services

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Data-driven Reactive Power Optimization of Distribution networks via graph Attention networks

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Journal of Modern Power Systems and Clean Energy 2024年第3期12卷 874-885页

作者： Wenlong Liao Dechang Yang Qi Liu Yixiong Jia Chenxi Wang Zhe Yang Wind Engineering and Renewable Energy Laboratory Ecole Polytechnique Federale de Lausanne(EPFL)Lausanne 1015Switzerland the College of Information and Electrical Engineering China Agricultural UniversityBeijing 100083China the College of Electrical Engineering and Automation Shandong University of Science and TechnologyQingdao 266590China the Department of Electrical and Electronic Engineering(Energy Digitalization Laboratory) The University of Hong KongHong KongChina the Department of Electrical Engineering The Hong Kong Polytechnic UniversityHong KongChina

Reactive power optimization of distribution networks is traditionally addressed by physical model based methods,which often lead to locally optimal solutions and require heavy online inference time *** improve the quality of the solution and reduce the inference time burden,this paper proposes a new graph attention networks based method to directly map the complex nonlinear relationship between graphs(topology and power loads)and reactive power scheduling schemes of distribution networks,from a data-driven *** graph attention network is tailored specifically to this problem and incorporates several innovative features such as a self-loop in the adjacency matrix,a customized loss function,and the use of max-pooling ***,a rulebased strategy is proposed to adjust infeasible solutions that violate *** results on multiple distribution networks demonstrate that the proposed method outperforms other machine learning based methods in terms of the solution quality and robustness to varying load ***,its online inference time is significantly faster than traditional physical model based methods,particularly for large-scale distribution networks.

关键词： Reactive power optimization graph neural network distribution network machine learning data-driven

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Multi-faceted spatio-temporal network for weather-aware air traffic flow prediction in multi-airport system

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Chinese Journal of Aeronautics 2024年第7期37卷 301-316页

作者： Kaiquan CAI Shuo TANG Shengsheng QIAN Zhiqi SHEN Yang YANG School of Electronic and Information Engineering Beihang UniversityBeijing 100191China State Key Laboratory of CNS/ATM Beijing100191China Institute of Automation Chinese Academy of SciencesBeijing 100190China Research Institute for Frontier Science Beihang UniversityBeijing 100191China

As one of the core modules for air traffic flow management,Air Traffic Flow Prediction(ATFP)in the Multi-Airport System(MAS)is a prerequisite for demand and capacity balance in the complex meteorological *** to the challenge of implicit interaction mechanism among traffic flow,airspace capacity and weather impact,the Weather-aware ATFP(Wa-ATFP)is still a nontrivial *** this paper,a novel Multi-faceted Spatio-Temporal graph Convolutional network(MSTGCN)is proposed to address the Wa-ATFP within the complex operations of ***,a spatio-temporal graph is constructed with three different nodes,including airport,route,and fix to describe the topology structure of ***,a weather-aware multi-faceted fusion module is proposed to integrate the feature of air traffic flow and the auxiliary features of capacity and weather,which can effectively address the complex impact of severe weather,e.g.,***,to capture the latent connections of nodes,an adaptive graph connection constructor is *** experimental results with the real-world operational dataset in Guangdong-Hong Kong-Macao Greater Bay Area,China,validate that the proposed approach outperforms the state-of-the-art machine-learning and deep-learning based baseline approaches in performance.

关键词： Air traffic control graph neural network Multi-faceted information Air traffic flow prediction Multi-airport system

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A novel graph oversampling framework for node classification in class-imbalanced graphs

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Science China(Information Sciences) 2024年第6期67卷 214-229页

作者： Riting XIA Chunxu ZHANG Yan ZHANG Xueyan LIU Bo YANG Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University College of Artificial Intelligence Jilin University College of Computer Science and Technology Jilin University College of Communication Engineering Jilin University

graph neural network(GNN) is a promising method to analyze graphs. Most existing GNNs adopt the class-balanced assumption, which cannot deal with class-imbalanced graphs well. The oversampling technique is effective in alleviating class-imbalanced problems. However, most graph oversampling methods generate synthetic minority nodes and their edges after applying GNNs. They ignore the problem that the representations of the original and synthetic minority nodes are dominated by majority nodes caused by aggregating neighbor information through GNN before oversampling. In this paper, we propose a novel graph oversampling framework, termed distribution alignment-based oversampling for node classification in classimbalanced graphs(named graph-DAO). Our framework generates synthetic minority nodes before GNN to avoid the dominance of majority nodes caused by message passing in GNNs. Additionally, we introduce a distribution alignment method based on the sum-product network to learn more information about minority nodes. To our best knowledge, it is the first to use the sum-product network to solve the class-imbalanced problem in node classification. A large number of experiments on four real datasets show that our method achieves the optimal results on the node classification task for class-imbalanced graphs.

关键词： graph neural network class-imbalanced graphs sum-product network oversampling node classification

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Multi-layer network embedding on scc-based network with motif

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Digital Communications and networks 2024年第3期10卷 546-556页

作者： Lu Sun Xiaona Li Mingyue Zhang Liangtian Wan Yun Lin Xianpeng Wang Gang Xu Department of Communication Engineering Institute of Information Science TechnologyDalian Maritime UniversityDalian116026China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and TelecommunicationsChongqing400065China Key Laboratory for Ubiquitous network and Service Software of Liaoning Province DUT School of Software Technology&DUT-RU International School of Information Science and EngineeringDalian University of TechnologyDalian 116620China College of Information and Communication Engineering Harbin Engineering UniversityHarbin 150001China School of Information and Communication Engineering Hainan UniversityHaikou 570228China State Key Laboratory of Millimeter Waves School of Information Science and EngineeringSoutheast UniversityNanjing 210096China

Interconnection of all things challenges the traditional communication methods,and Semantic Communication and Computing(SCC)will become new *** is a challenging task to accurately detect,extract,and represent semantic information in the research of SCC-based *** previous research,researchers usually use convolution to extract the feature information of a graph and perform the corresponding task of node ***,the content of semantic information is quite *** graph convolutional neural networks provide an effective solution for node classification tasks,due to their limitations in representing multiple relational patterns and not recognizing and analyzing higher-order local structures,the extracted feature information is subject to varying degrees of ***,this paper extends from a single-layer topology network to a multi-layer heterogeneous topology *** Bidirectional Encoder Representations from Transformers(BERT)training word vector is introduced to extract the semantic features in the network,and the existing graph neural network is improved by combining the higher-order local feature module of the network model representation network.A multi-layer network embedding algorithm on SCC-based networks with motifs is proposed to complete the task of end-to-end node *** verify the effectiveness of the algorithm on a real multi-layer heterogeneous network.

关键词： Semantic communication and computing Multi-layer network graph neural network Motif

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Learning to Branch in Combinatorial Optimization With graph Pointer networks

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 157-169页

作者： Rui Wang Zhiming Zhou Kaiwen Li Tao Zhang Ling Wang Xin Xu Xiangke Liao IEEE the Xiangjiang Laboratory and the College of Systems Engineering National University of Defense TechnologyChangsha 410073China the Institute of Automation Chinese Academy of SciencesBeijing 100190China the College of Systems Engineering National University of Defense TechnologyChangsha 410073 the Hunan Key Laboratory of Multi-Energy System Intelligent Interconnection Technology Changsha 410073China the Department of Automation Tsinghua UniversityBeijing 100084China the College of Intelligence Science and Technology National University of Defense TechnologyChangsha 410073China the College of Computer Science and Technology National University of Defense TechnologyChangsha 410073China

Traditional expert-designed branching rules in branch-and-bound(B&B) are static, often failing to adapt to diverse and evolving problem instances. Crafting these rules is labor-intensive, and may not scale well with complex *** the frequent need to solve varied combinatorial optimization problems, leveraging statistical learning to auto-tune B&B algorithms for specific problem classes becomes attractive. This paper proposes a graph pointer network model to learn the branch rules. graph features, global features and historical features are designated to represent the solver state. The graph neural network processes graph features, while the pointer mechanism assimilates the global and historical features to finally determine the variable on which to branch. The model is trained to imitate the expert strong branching rule by a tailored top-k Kullback-Leibler divergence loss function. Experiments on a series of benchmark problems demonstrate that the proposed approach significantly outperforms the widely used expert-designed branching rules. It also outperforms state-of-the-art machine-learning-based branch-and-bound methods in terms of solving speed and search tree size on all the test instances. In addition, the model can generalize to unseen instances and scale to larger instances.

关键词： Branch-and-bound(B&B) combinatorial optimization deep learning graph neural network imitation learning

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