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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: Many application domains in the real world are heavily dependent on graph data.However, graph applications are vastly different from traditional applications.It is inefficient to use general-purpose platforms for graph applications, thus contributing to the researchof 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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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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Technologies and Applications of Big Data Knowledge Engineering for Smart Taxation Systems

Zheng Qinghua , Shi Bin , Dong Bo

Strategic Study of CAE 2023, Volume 25, Issue 2,   Pages 221-231 doi: 10.15302/J-SSCAE-2023.07.005

Abstract:

Taxation is vital for national governance, and the digital transformation of governments necessitates smart taxation. Therefore, analyzing the key issues and exploring the development ideas for smart taxation is of both theoretical and practical values. In this study, following an analysis of the development status and challenges facing China’s intelligent taxation field, we proposed a big data knowledge engineering approach that emphasizes data knowledgeization, knowledge systematization, and knowledge reasonability, and developed a five-layer technical architecture that consists of knowledge sources, knowledge extraction, knowledge mapping, knowledge reasoning, and application layers. After elaborating the representative application scenarios including knowledge-driven tax preference calculation, interpretable tax risk identification, intelligent decision support for tax policies, and smart tax questioning,we investigated the limitations of the proposed approach and further discussed the directions for future research. Furthermore, we proposed the following development suggestions in terms of data, technology, and ecology: (1) standardizing tax-related information and improving the national data sharing, opening, and guarantee system; (2) integrating the achievements of various information disciplines and improving the application system of big data knowledge engineering for smart taxation; and (3) promoting talent training and the development of technical standards for big data knowledge engineering.

Keywords： smart taxation     knowledge engineering     big data     knowledge graph     knowledge reasoning    

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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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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 graph

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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A DNA Computing Model for the Graph Vertex Coloring Problem Based on a Probe Graph Article

Jin Xu, Xiaoli Qiang, Kai Zhang, Cheng Zhang, Jing Yang

Engineering 2018, Volume 4, Issue 1,   Pages 61-77 doi: 10.1016/j.eng.2018.02.011

Abstract: overcome this bottleneck and improve the processing speed, we propose a DNA computing model to solve the graphIn this article, a 3-colorable graph with 61 vertices is used to illustrate the capability of the DNAThe experiment showed that not only are all the solutions of the graph found, but also more than 99%

Keywords： DNA computing     Graph vertex coloring problem     Polymerase chain reaction    

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Stochastic extra-gradient based alternating direction methods for graph-guided regularizedminimization None

Qiang LAN, Lin-bo QIAO, Yi-jie WANG

Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 6,   Pages 755-762 doi: 10.1631/FITEE.1601771

Abstract: extra-gradient alternating direction method with augmented Lagrangian function (SEGAL), to minimize the graph-guidedA number of important applications in machine learning follow the graph-guided optimization formulationWe conduct experiments on fused logistic regression and graph-guided regularized regression.

Keywords： Stochastic optimization     Graph-guided minimization     Extra-gradient method     Fused logistic regression     Graph-guided    

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Special issue: Innovative applications of big data and artificial intelligence

Frontiers of Engineering Management 2022, Volume 9, Issue 4,   Pages 517-519 doi: 10.1007/s42524-022-0234-0

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Interpreting the vulnerability of power systems in cascading failures using multi-graph convolutional Research Article

Supaporn LONAPALAWONG, Changsheng CHEN, Can WANG, Wei CHEN

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 12,   Pages 1848-1861 doi: 10.1631/FITEE.2200035

Abstract: system's spatial information such as the electrical distance to increase the accuracy in the process of graphuses power system topology and spatial information to optimize the edge weight assignment of the line graphThen we propose a multi-graph convolutional network (MGCN) based on a graph classification task, which

Keywords： Power systems     Vulnerability     Cascading failures     Multi-graph convolutional networks     Weighted line graph    

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Erratum to: Efficient keyword search over graph-structured data based on minimal covered Erratum

Asieh Ghanbarpour, Abbas Niknafs, Hassan Naderi,naderi@iust.ac.ir

Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 6,   Pages 809-962 doi: 10.1631/FITEE.18e0133

Abstract: Unfortunately the second author’s name has been misspelt. It should be read: Abbas NIKNAFS.
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Paper evolution graph: multi-view structural retrieval for academic literature None

Dan-ping LIAO, Yun-tao QIAN

Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 2,   Pages 187-205 doi: 10.1631/FITEE.1700105

Abstract: method to build structural retrieval results for academic literature, which we call a paper evolution graph

Keywords： Paper evolution graph     Academic literature retrieval     Metagraph factorization     Topic coherence    

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Progress of Brain Network Studies on Anesthesia and Consciousness: Framework and Clinical Applications Review

Jun Liu, Kangli Dong, Yi Sun, Ioannis Kakkos, Fan Huang, Guozheng Wang, Peng Qi, Xing Chen, Delin Zhang, Anastasios Bezerianos, Yu Sun

Engineering 2023, Volume 20, Issue 1,   Pages 77-95 doi: 10.1016/j.eng.2021.11.013

Abstract:

Although the relationship between anesthesia and consciousness has been investigated for decades, our understanding of the underlying neural mechanisms of anesthesia and consciousness remains rudimentary, which limits the development of systems for anesthesia monitoring and consciousness evaluation. Moreover, the current practices for anesthesia monitoring are mainly based on methods that do not provide adequate information and may present obstacles to the precise application of anesthesia. Most recently, there has been a growing trend to utilize brain network analysis to reveal the mechanisms of anesthesia, with the aim of providing novel insights to promote practical application. This review summarizes recent research on brain network studies of anesthesia, and compares the underlying neural mechanisms of consciousness and anesthesia along with the neural signs and measures of the distinct aspects of neural activity. Using the theory of cortical fragmentation as a starting point, we introduce important methods and research involving connectivity and network analysis. We demonstrate that whole-brain multimodal network data can provide important supplementary clinical information. More importantly, this review posits that brain network methods, if simplified, will likely play an important role in improving the current clinical anesthesia monitoring systems.

Keywords： Anesthesia     Brain network     Connectivity     Graph theoretical analysis     Clinical monitoring system    

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Title Author Date Type Operation

Large-scale graph processing systems: a survey

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

Journal Article

Classifying multiclass relationships between ASes using graph convolutional network

Journal Article

Technologies and Applications of Big Data Knowledge Engineering for Smart Taxation Systems

Zheng Qinghua , Shi Bin , Dong Bo

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

Journal Article

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

Journal Article

A Practical Approach to Constructing a Knowledge Graph for Cybersecurity

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

Journal Article

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

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

Journal Article

A DNA Computing Model for the Graph Vertex Coloring Problem Based on a Probe Graph

Jin Xu, Xiaoli Qiang, Kai Zhang, Cheng Zhang, Jing Yang

Journal Article

Stochastic extra-gradient based alternating direction methods for graph-guided regularizedminimization

Qiang LAN, Lin-bo QIAO, Yi-jie WANG

Journal Article

Special issue: Innovative applications of big data and artificial intelligence

Journal Article

Interpreting the vulnerability of power systems in cascading failures using multi-graph convolutional

Supaporn LONAPALAWONG, Changsheng CHEN, Can WANG, Wei CHEN

Journal Article

Erratum to: Efficient keyword search over graph-structured data based on minimal covered

Asieh Ghanbarpour, Abbas Niknafs, Hassan Naderi,naderi@iust.ac.ir

Journal Article

Paper evolution graph: multi-view structural retrieval for academic literature

Dan-ping LIAO, Yun-tao QIAN

Journal Article

Progress of Brain Network Studies on Anesthesia and Consciousness: Framework and Clinical Applications

Jun Liu, Kangli Dong, Yi Sun, Ioannis Kakkos, Fan Huang, Guozheng Wang, Peng Qi, Xing Chen, Delin Zhang, Anastasios Bezerianos, Yu Sun

Journal Article