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NGAT: attention in breadth and depth exploration for semi-supervised graph representation learning Research Articles

Jianke HU, Yin ZHANG,yinzh@zju.edu.cn

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 3, Pages 409-421 doi: 10.1631/FITEE.2000657

Abstract: Recently, graph neural networks (GNNs) have achieved remarkable performance in representation learningon graph-structured data.To alleviate oversmoothing, we propose a nested graph network (NGAT), which can work in a semi-supervised

Keywords： Graph learning Semi-supervised learning Node classification Attention

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Imbalanced fault diagnosis of rotating machinery using autoencoder-based SuperGraph feature learning

Frontiers of Mechanical Engineering 2021, Volume 16, Issue 4, Pages 829-839 doi: 10.1007/s11465-021-0652-4

Abstract: problem, an imbalanced fault diagnosis of rotating machinery using autoencoder-based SuperGraph feature learningAnd the edge connections in the graph depend on the relationship between signals.On the basis, graph convolution is performed on the constructed SuperGraph to achieve imbalanced trainingeffectively achieve rotating machinery fault diagnosis towards imbalanced training dataset through graphfeature learning.

Keywords： imbalanced fault diagnosis graph feature learning rotating machinery autoencoder

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Deep reinforcement learning-based critical element identification and demolition planning of frame structures

Shaojun ZHU; Makoto OHSAKI; Kazuki HAYASHI; Shaohan ZONG; Xiaonong GUO

Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 11, Pages 1397-1414 doi: 10.1007/s11709-022-0860-y

Abstract: indices considering the severity of the ultimate collapse scenario are proposed using reinforcement learningand graph embedding.estimation of the Q values, and handle problems with different action spaces owing to utilization of graphmethods, it is demonstrated that the computational cost is considerably reduced because the reinforcement learningBesides, it is proved that the Q values produced by the reinforcement learning agent can make

Keywords： progressive collapse alternate load path demolition planning reinforcement learning graph embedding

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The Group Interaction Field for Learning and Explaining Pedestrian Anticipation

Xueyang Wang,Xuecheng Chen,Puhua Jiang,Haozhe Lin,Xiaoyun Yuan,Mengqi Ji,Yuchen Guo,Ruqi Huang,Lu Fang,

Engineering doi: 10.1016/j.eng.2023.05.020

Abstract: GIFNet quantifies the influence of group behaviors by formulating a group interaction graph with propagationand graph attention that is adaptive to the group size and dynamic interaction states.

Keywords： behavior modeling and prediction Implicit representation of pedestrian anticipation Group interaction Graph

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Classifying multiclass relationships between ASes using graph convolutional network

Frontiers of Engineering Management Pages 653-667 doi: 10.1007/s42524-022-0217-1

Abstract: We then introduce new features and propose a graph convolutional network (GCN) framework, AS-GCN, to

Keywords： autonomous system multiclass relationship graph convolutional network classification algorithm Internet

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Self-supervised graph learning with target-adaptive masking for session-based recommendation Research Article

Yitong WANG, Fei CAI, Zhiqiang PAN, Chengyu SONG,wangyitong20@nudt.edu.cn,caifei08@nudt.edu.cn,panzhiqiang@nudt.edu.cn,songchengyu@nudt.edu.cn

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 1, Pages 73-87 doi: 10.1631/FITEE.2200137

Abstract: To tackle the above issues, we propose a self-supervised graph learning with (SGL-TM) method.Specifically, we first construct a global graph based on all involved sessions and subsequently capture

Keywords： Session-based recommendation Self-supervised learning Graph neural networks Target-adaptive masking

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Soft-HGRNs: soft hierarchical graph recurrent networks for multi-agent partially observable environments Research Article

Yixiang REN, Zhenhui YE, Yining CHEN, Xiaohong JIANG, Guanghua SONG,yixiangren@zju.edu.cn,zhenhuiye@zju.edu.cn,ch19930611@zju.edu.cn,ghsong@zju.edu.cn

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 1, Pages 117-130 doi: 10.1631/FITEE.2200073

Abstract: and remembering his/her own experience, we propose a novel network structure called the hierarchical graphSpecifically, we construct the multi-agent system as a graph, use a novel graph convolution structure

Keywords： Deep reinforcement learning Graph-based communication Maximum-entropy learning Partial observability

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financially constrained small- and medium-sized enterprises based on a multi-relation translational graph Research Article

Qianqiao LIANG, Hua WEI, Yaxi WU, Feng WEI, Deng ZHAO, Jianshan HE, Xiaolin ZHENG, Guofang MA, Bing HAN

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 3, Pages 388-402 doi: 10.1631/FITEE.2200151

Abstract: To address these challenges, we propose a graph neural network named Multi-relation tRanslatIonal GrapH

Keywords： Financing needs exploration Graph representation learning Transfer heterogeneity Behavior heterogeneity

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Large-scale graph processing systems: a survey Review

Ning LIU, Dong-sheng LI, Yi-ming ZHANG, Xiong-lve LI

Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 3, Pages 384-404 doi: 10.1631/FITEE.1900127

Abstract: Graph is a significant data structure that describes the relationship between entries.Many application domains in the real world are heavily dependent on graph data.However, graph applications are vastly different from traditional applications.of specific graph processing platforms.In this survey, we systematically categorize the graph workloads and applications, and provide a detailed

Keywords： Graph workloads Graph applications Graph processing systems

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Detecting large-scale underwater cracks based on remote operated vehicle and graph convolutional neural

Wenxuan CAO; Junjie LI

Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 11, Pages 1378-1396 doi: 10.1007/s11709-022-0855-8

Abstract: The graph convolutional neural network (GCN) was used to segment the stitched image.

Keywords： underwater cracks remote operated vehicle image stitching image segmentation graph convolutional

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Distributed coordination inmulti-agent systems: a graph Laplacian perspective

Zhi-min HAN,Zhi-yun LIN,Min-yue FU,Zhi-yong CHEN

Frontiers of Information Technology & Electronic Engineering 2015, Volume 16, Issue 6, Pages 429-448 doi: 10.1631/FITEE.1500118

Abstract: This paper reviews some main results and progress in distributed multi-agent coordination from a graphsurvey of existing literature in distributed multi-agent coordination and a new perspective in terms of graphFor different types of graph Laplacians, we summarize their inherent coordination features and specific

Keywords： Multi-agent systems Distributed coordination Graph Laplacian

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Efficacy of intelligent diagnosis with a dynamic uncertain causality graph model for rare disorders of

Dongping Ning, Zhan Zhang, Kun Qiu, Lin Lu, Qin Zhang, Yan Zhu, Renzhi Wang

Frontiers of Medicine 2020, Volume 14, Issue 4, Pages 498-505 doi: 10.1007/s11684-020-0791-8

Abstract: On the basis of the principles and algorithms of dynamic uncertain causality graph (DUCG), a diagnosis

Keywords： disorders of sex development (DSD) intelligent diagnosis dynamic uncertain causality graph

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EncyCatalogRec: catalog recommendation for encyclopedia article completion Article

Wei-ming LU, Jia-hui LIU, Wei XU, Peng WANG, Bao-gang WEI

Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 3, Pages 436-447 doi: 10.1631/FITEE.1800363

Abstract: Then a relation graph is built from the articles and the candidate items.This is further transformed into a product graph.So, the recommendation problem is changed to a transductive learning problem in the product graph.Finally, the recommended items are sorted by the learning-to-rank technology.

Keywords： Catalog recommendation Encyclopedia article completion Product graph Transductive learning

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A Practical Approach to Constructing a Knowledge Graph for Cybersecurity Article

Yan Jia, Yulu Qi, Huaijun Shang, Rong Jiang, Aiping Li

Engineering 2018, Volume 4, Issue 1, Pages 53-60 doi: 10.1016/j.eng.2018.01.004

Abstract: At present, it is very significant that certain scholars have combined the concept of the knowledge graphUsing machine learning, we extract entities and build ontology to obtain a cybersecurity knowledge base

Keywords： Cybersecurity Knowledge graph Knowledge deduction

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Erratum to: Soft-HGRNs: soft hierarchical graph recurrent networks for multi-agent partially observable

Yixiang REN, Zhenhui YE, Yining CHEN, Xiaohong JIANG, Guanghua SONG

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 3, Pages 480-480 doi: 10.1631/FITEE.22e0073

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Title Author Date Type Operation

NGAT: attention in breadth and depth exploration for semi-supervised graph representation learning

Jianke HU, Yin ZHANG,yinzh@zju.edu.cn

Journal Article

Imbalanced fault diagnosis of rotating machinery using autoencoder-based SuperGraph feature learning

Journal Article

Deep reinforcement learning-based critical element identification and demolition planning of frame structures

Shaojun ZHU; Makoto OHSAKI; Kazuki HAYASHI; Shaohan ZONG; Xiaonong GUO

Journal Article

The Group Interaction Field for Learning and Explaining Pedestrian Anticipation

Xueyang Wang,Xuecheng Chen,Puhua Jiang,Haozhe Lin,Xiaoyun Yuan,Mengqi Ji,Yuchen Guo,Ruqi Huang,Lu Fang,

Journal Article

Classifying multiclass relationships between ASes using graph convolutional network

Journal Article

Self-supervised graph learning with target-adaptive masking for session-based recommendation

Yitong WANG, Fei CAI, Zhiqiang PAN, Chengyu SONG,wangyitong20@nudt.edu.cn,caifei08@nudt.edu.cn,panzhiqiang@nudt.edu.cn,songchengyu@nudt.edu.cn

Journal Article

Soft-HGRNs: soft hierarchical graph recurrent networks for multi-agent partially observable environments

Yixiang REN, Zhenhui YE, Yining CHEN, Xiaohong JIANG, Guanghua SONG,yixiangren@zju.edu.cn,zhenhuiye@zju.edu.cn,ch19930611@zju.edu.cn,ghsong@zju.edu.cn

Journal Article

financially constrained small- and medium-sized enterprises based on a multi-relation translational graph

Qianqiao LIANG, Hua WEI, Yaxi WU, Feng WEI, Deng ZHAO, Jianshan HE, Xiaolin ZHENG, Guofang MA, Bing HAN

Journal Article

Large-scale graph processing systems: a survey

Ning LIU, Dong-sheng LI, Yi-ming ZHANG, Xiong-lve LI

Journal Article

Detecting large-scale underwater cracks based on remote operated vehicle and graph convolutional neural

Wenxuan CAO; Junjie LI

Journal Article

Distributed coordination inmulti-agent systems: a graph Laplacian perspective

Zhi-min HAN,Zhi-yun LIN,Min-yue FU,Zhi-yong CHEN