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4th International Conference on Smart Power and Internet Energy Systems (SPIES)

作者： Jiao Fei Ma Zhenyuan Xu Jiannan Tan Yuanpeng Duan Minghui Tong Jie China Elect Power Res Inst Artificial Intelligence Applicat Dept Beijing Peoples R China Tianjin Elect Power Corp Elect Power Res Inst Dept Condit Evaluat Power Equipment Tianjin Peoples R China

ISBN: (纸本)9781665489577

Power equipment inspection aims to discover and eliminate equipment defects and potential safety hazards timely and ensure the safe operation of equipment. Now a large number of graph structure data have emerged in the equipment inspection field to improve the organization, management, and cognitive ability of knowledge. As a key step in the inspection business, equipment defect grading is used to determine the severity that affects the safe operation of equipment, so the accuracy and generalization ability of the model are required to be high. The pooling operation of traditional graph neural networks has the problem of information loss. Therefore, this paper introduces the multi-scale mechanism to improve the accuracy and generalization ability of the model and realize the accurate grading of equipment defects. By conducting experiments on NCI-1 and CEPRI_EQUIP datasets and comparing the proposed algorithm with baseline methods, the significant performance of the proposed algorithm is verified.

关键词： electric power equipment defect grading graph computing multi-scale mechanism

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graphScope Flex: LEGO-like graph computing Stack 24

GraphScope Flex: LEGO-like Graph Computing Stack

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ACM International Conference on Management of Data (SIGMOD)

作者： He, Tao Hu, Shuxian Lai, Longbin Li, Dongze Li, Neng Li, Xue Liu, Lexiao Luo, Xiaojian Lyu, Binqing Meng, Ke Shen, Sijie Su, Li Wang, Lei Xu, Jingbo Yu, Wenyuan Zeng, Weibin Zhang, Lei Zhang, Siyuan Zhou, Jingren Zhou, Xiaoli Zhu, Diwen Alibaba Grp Hangzhou Peoples R China

ISBN: (纸本)9798400704222

graph computing has become increasingly crucial in processing large-scale graph data, with numerous systems developed for this purpose. Two years ago, we introduced graphScope as a system addressing a wide array of graph computing needs, including graph traversal, analytics, and learning in one system. Since its inception, graphScope has achieved significant technological advancements and gained widespread adoption across various industries. However, one key lesson from this journey has been understanding the limitations of a "one-size-fits-all" approach, especially when dealing with the diversity of programming interfaces, applications, and data storage formats in graph computing. In response to these challenges, we present graphScope Flex, the next iteration of graphScope. graphScope Flex is designed to be both resource-efficient and cost-effective, while also providing flexibility and user-friendliness through its LEGO-like modularity. This paper explores the architectural innovations and fundamental design principles of graphScope Flex, all of which are direct outcomes of the lessons learned during our ongoing development process. We validate the adaptability and efficiency of graphScope Flex with extensive evaluations on synthetic and real-world datasets. The results show that graphScope Flex achieves 2.4x throughput and up to 55.7x speedup over other systems on the LDBC Social Network and graphalytics benchmarks, respectively. Furthermore, graphScope Flex accomplishes up to a 2,400x performance gain in real-world applications, demonstrating its proficiency across a wide range of graph computing scenarios with increased effectiveness.

关键词： graph computing distributed system graph analytics graph query

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Dynamic graph computing: A method of finding companion vehicles from traffic streaming data

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INFORMATION SCIENCES 2022年 591卷 128-141页

作者： Xiao, Yunpeng He, Xi Yang, Chen Liu, Haoyu Liu, Yanbing Chongqing Univ Posts & Telecommun Sch Software Engn Chongqing 400065 Peoples R China

For intelligent transportation research, the detection of companion vehicle patterns aids in excavating behavioral relation between vehicles. This paper builds a dynamic license plate corpus based on streaming automatic number plate recognition data and combines the monitoring cameras character division to mine companion vehicle groups in real time. First, we establish the traffic flow graph based on vehicle trajectory data, and the improved PageRank algorithm is used to obtain the influence of the monitoring cameras. Second, the time-sliding window mechanism in Spark Streaming is applied to the streaming data, and the creation and updating of vehicle dynamic license plate corpus are completed according to the driving trajectory data. Finally, the plate-number dynamic graph computing algorithm is proposed to establish the dynamic relation graph between vehicles based on dynamic corpus and character identification of monitoring cameras. The camera characters are used as the influencing factor to correlate with the graph formed by vehicle nodes. The companion groups are obtained by real-time calculation of the trim and weight in the graph. Experimental analysis on the real license plate recognition dataset shows that the proposed model can effectively reduce the complexity of data processing and can detect companion vehicle groups in real time (c) 2022 Elsevier Inc. All rights reserved.

关键词： Role division Streaming data graph computing Companion vehicle

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graph Database and graph computing for Power System Analysis 1

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丛书名： IEEE Press Series on Power and Energy Systems

2023年

作者： Renchang Dai Guangyi Liu

ISBN: (数字)9781119903895

ISBN: (纸本)9781119903864

graph Database and graph computing for Power System Analysis Understand a new way to model power systems with this comprehensive and practical guide graph databases have become one of the essential tools for managing large data systems. Their structure improves over traditional table-based relational databases in that it reconciles more closely to the inherent physics of a power system, enabling it to model the components and the network of a power system in an organic way. The authors’ pioneering research has demonstrated the effectiveness and the potential of graph data management and graph computing to transform power system analysis. graph Database and graph computing for Power System Analysis presents a comprehensive and accessible introduction to this research and its emerging applications. Programs and applications conventionally modeled for traditional relational databases are reconceived here to incorporate graph computing. The result is a detailed guide which demonstrates the utility and flexibility of this cutting-edge technology. The book’s readers will also find: Design configurations for a graph-based program to solve linear equations, differential equations, optimization problems, and more Detailed demonstrations of graph-based topology analysis, state estimation, power flow analysis, security-constrained economic dispatch, automatic generation control, small-signal stability, transient stability, and other concepts, analysis, and applications An authorial team with decades of experience in software design and power systems analysis graph Database and graph computing for Power System Analysis is essential for researchers and academics in power systems analysis and energy-related fields, as well as for advanced graduate students looking to understand this particular set of technologies.

关键词： Social networks systems modeling graph computing large-scale algebraic questions high-dimensional differential equations optimization problems parallel computing machine learning topology analysis state estimation power flow contingency analysis economic dispatch automatic generation control small-signal stability transient stability

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Fast Cycle Structure Detection for Power Grids Based on graph computing

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CSEE Journal of Power and Energy Systems 2023年第6期9卷 2204-2213页

作者： Xinqiao Wang Kewen Liu Weijiang Lu Ting Zhao Baohua Zhao Xiaoming Liu the Computing&Application Technology Research Division Global Energy Interconnection Research InstituteBeijing 102209China

The cycle structure in a power grid may lower the stability of the network;thus,it is of great significance to accu-rately and timely detect cycles in power grid ***,detecting possible cycles in a large-scale network can be highly time consuming and computationally *** addition,since the power grid's topology changes over time,cycles can appear and disappear,and it can be difficult to monitor them in real *** traditional computing systems,cycle detection requires considerable computational resources,making real-time cycle detection in large-scale power grids an impossible *** computing has shown excellent performance in many areas and has solved many practical graph-related problems,such as power flow calculation and state *** this article,a cycle detection method,the Paton method,is implemented and optimized on a graph computing *** cases are used to test its performance in an actual power grid topology *** results show that the graph computing-based Paton method reduces the time consumption by at least 60%compared to that of other methods.

关键词： Cycle detection graph computing power grid distribution grid

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graph computing System and Application Based on Large-Scale Information Network 1

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5th International Conference on Space Information Networks (SINC)

作者： Xu, Jingbo Li, Zhao Zeng, Weibin Huang, Jiaming Peking Univ Beijing Peoples R China Alibaba Grp Hangzhou Peoples R China

ISBN: (数字)9789811619670

ISBN: (纸本)9789811619663;9789811619670

graph computing is more and more widely used in various fields such as spatial information network and social network. However, the existing graph computing systems have some problems like complex programming and steep learning curve. This paper introduces GRAPE, a distributed large-scale graph Engine, which has the unique features of solid theoretical guarantee, ease of use, auto-parallelization and high performance. The paper also introduces several typical scenarios of graph computing, including entity resolution, link prediction, community detection and graph mining of spatial information network. In these scenarios, various problems have been encountered in the existing systems, such as failure to compute over large-scale data due to the high computation complexity, loss of accuracy due to the cropping of original data and too long execution time. In the face of these challenges, GRAPE is easy to support these computing scenarios with a series of technical improvements. With the deployment of GRAPE in Alibaba, both effectiveness and efficiency of graph computing have been greatly improved.

关键词： graph computing Distributed system Incremental evaluation Information network

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EPgraph: An Efficient graph computing Model in Persistent Memory System 20

EPGraph: An Efficient Graph Computing Model in Persistent Me...

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20th IEEE Int Symposium on Parallel and Distributed Processing with Applicat / 15th IEEE Int Conf on Social Comp and Networking / 12th IEEE Int Conf on Big Data and Cloud Comp / 12th IEEE Int Conf on Sustainable Comp and Commun

作者： Li, Baoke Cao, Cong Yuan, Fangfang Liu, Yanbing Li, Baohui Fang, Binxing Chinese Acad Sci Inst Informat Engn Beijing Peoples R China Univ Chinese Acad Sci Sch Cyber Secur Beijing Peoples R China Chinese Informat Technol Secur Evaluat Ctr Dept Syst Evaluat Beijing Peoples R China UESTC Guangdong Inst Elect & Informat Engn Dongguan Guangdong Peoples R China

ISBN: (纸本)9781665464970

With the wide range of requirements to analyse large-scale graphs in many real-world applications (e.g., relationship analysis, fraud detection and product recommendation), graph computing recently receives intensive interests. However, the massive volume and the power-law distribution of graphs are objective obstacles to efficient graph computing. Fortunately, Intel Optane DC Persistent Memory (PMEM) has emerged as a new solution, which is expected to play a crucial role in large-scale graph processing. But compared with main memory, PMEM shows much lower bandwidth and higher access latency. Therefore, it becomes paramount to fully exploit the advantages of PMEM in persistent memory systems. In this paper, we propose EPgraph, a novel efficient graph computing model designed by PMEM. To a considerable extent, it improves the spatial locality and the temporal locality of graph computing at the same time. The main contribution of our work lies in three aspects. Firstly, we design a degree-based data layering strategy to reduce the impact of power-law distribution. The hierarchical strategy makes full use of DRAM and PMEM simultaneously. Secondly, we propose a dynamic migration mechanism during the iterative execution of graph computing. The dynamic mechanism equitably schedules the subgraphs which are used in the next iteration. Thirdly, we evaluate the effectiveness of EPgraph on five public graph data sets. Extensive evaluation results show that EPgraph outperforms state-of-the-art graph computing systems by 22.67%-35.03%.

关键词： High performance model persistent memory system graph computing data layering dynamic data migration

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Implementation and Optimization of graph computing Algorithms Based on graph Database 1

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7th China Conference on Knowledge graph and Semantic computing (CCKS)

作者： Wei, Jiaqi Wu, Shuang Jia, Jinkang Liu, Ziqian China Telecom Cybersecur Technol Co Ltd Beijing Peoples R China

ISBN: (数字)9789811983009

ISBN: (纸本)9789811982996;9789811983009

Data mining and knowledge inferring over knowledge graph has gained particular attention and been widely applied in industry over the past years. These works can be generalized to graph computing and graph analysis tasks, such as shortest path search, hop-constrained reachability, PageRank, triangle counting, and closeness centrality computation. However, existing graph database query language (SPARQL, Gremlin, etc.) dose not implement these algorithms. Therefore, CCKS 2022 holds a benchmark of graph computing algorithm based on graph database. We implemented all these five algorithms mentioned above based on graph database and made many optimizations to improve computation efficiency. On the final leaderboard, we took the first place, which shows extremely high practicality and efficiency.

关键词： graph computing graph data management graph data analysis

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graphFusion: Integrating multi-level semantic information with graph computing for enhanced 3D instance segmentation

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NEUROcomputing 2024年 602卷

作者： Pan, Lei Luan, Wuyang Zheng, Yuan Li, Junhui Tao, Linwei Xu, Chang Civil Aviat Flight Univ China Sch Comp Sci Deyang 618300 Peoples R China Univ Sydney Sydney NSW 2006 Australia

graph computing has emerged as a focal point in recent research across various fields, including the realm of 3D instance segmentation, where it aids in detecting and segmenting objects within volumetric data. Our study introduces graphFusion, a state-of-the-art network that harnesses the power of graph computing to enhance the segmentation of 3D point clouds. graphFusion is equipped with a Multi-Level Semantic Aggregation Module, architectured akin to a graph, to capture comprehensive features from 3D point clouds. Utilizing graph-based methodologies, this module proficiently aggregates multi-scale semantic information, illuminating insights from both global and local contexts. Additionally, our Parallel Feature Fusion Transformer Module leverages graph- transformer techniques to intricately process complex spatial relationships within point clouds, culminating in a more cohesive feature representation. Rigorous experiments on the ScanNetv2 dataset affirm the dominance of graphFusion, which eclipses current methods by 2.2% in mean Average Precision (mAP) on the hidden test set. The model's code is accessible at https://***/3171228612/graphFusion.

关键词： 3D instance segmentation graph computing Transformer network

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A Doctor Recommendation Based on graph computing and LDA Topic Model

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2021年第1期14卷 808-817页

作者： Meng, Qiuqing Xiong, Huixiang Cent China Normal Univ Sch Informat Management Wuhan 403792 Peoples R China Guizhou Univ Financial & Econ Sch Informat Guiyang 550025 Peoples R China

Doctor recommendation technology can help patients filter out large number of irrelevant doctors and find doctors who meet their actual needs quickly and accurately, helping patients gain access to helpful personalized online healthcare services. 'co address the problems with the existing recommendation methods, this paper proposes a hybrid doctor recommendation model based on online healthcare platform, which utilizes the word2vec model, latent Dirichlet allocation (LDA) topic model, and other methods to find doctors who best suit patients' needs with the information obtained from consultations between doctors and patients. Then, the model treats these doctors as nodes in order to construct a doctor tag cooccurrence network and recommends the most important doctors in the network via an eigenvector centrality calculation model on the graph. This method identifies the important nodes in the entire effective doctor network to support the recommendation from a new graph computing perspective. An experiment conducted on the Chinese healthcare website *** proves that the proposed method a good recommendation performance. (C) 2021 The Authors. Published by Atlantis Press B.V.

关键词： Doctor recommendation LDA topic model Eigenvector centrality graph computing word2vec

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