Relevant bibliographies by topics / Graph Analytics

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Graph Analytics'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Graph Analytics"

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Meng, Lingkai, Yu Shao, Long Yuan, et al. "Revisiting Graph Analytics Benchmark." Proceedings of the ACM on Management of Data 3, no. 3 (2025): 1–28. https://doi.org/10.1145/3725345.

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The rise of graph analytics platforms has led to the development of various benchmarks for evaluating and comparing platform performance. However, existing benchmarks often fall short of fully assessing performance due to limitations in core algorithm selection, data generation processes (and the corresponding synthetic datasets), as well as the neglect of API usability evaluation. To address these shortcomings, we propose a novel graph analytics benchmark. First, we select eight core algorithms by extensively reviewing both academic and industrial settings. Second, we design an efficient and
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Haglin, David, David Trimm, and Pak Chung Wong. "Big graph visual analytics." Information Visualization 16, no. 3 (2016): 155–56. http://dx.doi.org/10.1177/1473871616679013.

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This special issue of Information Visualization explores the technical challenges and technology development opportunities of graph visual analytics arising from the trend of big data. Big graph visual analytics is about applying visualization and analytics techniques to gather, analyze, and understand big graphs and the knowledge behind them.
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Houshmand, Farzin, Mohsen Lesani, and Keval Vora. "Grafs: declarative graph analytics." Proceedings of the ACM on Programming Languages 5, ICFP (2021): 1–32. http://dx.doi.org/10.1145/3473588.

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Graph analytics elicits insights from large graphs to inform critical decisions for business, safety and security. Several large-scale graph processing frameworks feature efficient runtime systems; however, they often provide programming models that are low-level and subtly different from each other. Therefore, end users can find implementation and specially optimization of graph analytics error-prone and time-consuming. This paper regards the abstract interface of the graph processing frameworks as the instruction set for graph analytics, and presents Grafs, a high-level declarative specifica
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Burch, Michael. "Visual analytics of large dynamic digraphs." Information Visualization 16, no. 3 (2016): 167–78. http://dx.doi.org/10.1177/1473871616661194.

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In this article, we investigate the problem of visually representing and analyzing large dynamic directed graphs that consist of many vertices, edges, and time steps. With this work we do not primarily focus on graph details but more on achieving an overview about long graph sequences with the major focus to be scalable in vertex, edge, and time dimensions. To reach this goal, we first map each graph to a bipartite layout with vertices in the same order for each graph supporting a preservation of the viewer’s mental map. A sequence of graphs is placed in a left-to-right reading direction. To f
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Zheng, Nan, Meng Sun, and Ye Yang. "Visual Analysis of College Sports Performance Based on Multimodal Knowledge Graph Optimization Neural Network." Computational Intelligence and Neuroscience 2022 (July 1, 2022): 1–12. http://dx.doi.org/10.1155/2022/5398932.

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In this paper, through data analysis of multimodal knowledge graph optimized neural network and visual analysis of college students’ sports performance, we use huge graph, a graph database supporting distributed storage, to store domain knowledge in the form of the knowledge graph, use Spring Boot to build a server-side framework, use Vue framework combined with vis.js to visualize relational network graphs, and design and implement a knowledge-oriented. This paper proposes a visual analytics system based on the theory of visual analytics. Based on the idea of visual analytics, this paper pres
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Lenharth, Andrew, Donald Nguyen, and Keshav Pingali. "Parallel graph analytics." Communications of the ACM 59, no. 5 (2016): 78–87. http://dx.doi.org/10.1145/2901919.

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Bonifati, Angela, M. Tamer Ozsu, Yuanyuan Tian, Hannes Voigt, Wenyuan Yu, and enjie Zhang. "A Roadmap to Graph Analytics." ACM SIGMOD Record 53, no. 4 (2025): 43–51. https://doi.org/10.1145/3712311.3712323.

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Graphs are ubiquitous data structures used in a large spectrum of applications, spanning from transportation networks, financial networks, social networks, product-order transactions and biomedical applications [33]. A recent survey on the usage of graph applications from real users has highlighted the fact that analytics is the most time-consuming task as opposed to testing, cleaning and ETL [32].
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Leonard, Lorne, Alan M. MacEachren, and Kamesh Madduri. "Graph-based visual analysis for large-scale hydrological modeling." Information Visualization 16, no. 3 (2016): 205–16. http://dx.doi.org/10.1177/1473871616661868.

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This article reports on the development and application of a visual analytics approach to big data cleaning and integration focused on very large graphs, constructed in support of national-scale hydrological modeling. We explain why large graphs are required for hydrology modeling and describe how we create two graphs using continental United States heterogeneous national data products. The first smaller graph is constructed by assigning level-12 hydrological unit code watersheds as nodes. Creating and cleaning graphs at this scale highlight the issues that cannot be addressed without high-res
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Firmli, Soukaina, and Dalila Chiadmi. "A Scalable Data Structure for Efficient Graph Analytics and In-Place Mutations." Data 8, no. 11 (2023): 166. http://dx.doi.org/10.3390/data8110166.

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The graph model enables a broad range of analyses; thus, graph processing (GP) is an invaluable tool in data analytics. At the heart of every GP system lies a concurrent graph data structure that stores the graph. Such a data structure needs to be highly efficient for both graph algorithms and queries. Due to the continuous evolution, the sparsity, and the scale-free nature of real-world graphs, GP systems face the challenge of providing an appropriate graph data structure that enables both fast analytical workloads and fast, low-memory graph mutations. Existing graph structures offer a hard t
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Hua, Jie, Mao Lin Huang, Weidong Huang, and Chenglin Zhao. "Applying Graph Centrality Metrics in Visual Analytics of Scientific Standard Datasets." Symmetry 11, no. 1 (2019): 30. http://dx.doi.org/10.3390/sym11010030.

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Graphs are often used to model data with a relational structure and graphs are usually visualised into node-link diagrams for a better understanding of the underlying data. Node-link diagrams represent not only data entries in a graph, but also the relations among the data entries. Further, many graph drawing algorithms and graph centrality metrics have been successfully applied in visual analytics of various graph datasets, yet little attention has been paid to analytics of scientific standard data. This study attempts to adopt graph drawing methods (force-directed algorithms) to visualise sc
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Dissertations / Theses on the topic "Graph Analytics"

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Rawlani, Praynaa. "Graph analytics on relational databases." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/100670.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2014.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 99-100).<br>Graph analytics has become increasing popular in the recent years. Conventionally, data is stored in relational databases that have been refined over decades, resulting in highly optimized data processing engines. However, the awkwardness of expressing iterative queries in SQL makes the relational query-processing model inadequate for graph analytics, leading to many alternativ
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Júnior, Alcebíades Dal Col. "Visual analytics via graph signal processing." Universidade de São Paulo, 2018. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-22102018-112358/.

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The classical wavelet transform has been widely used in image and signal processing, where a signal is decomposed into a combination of basis signals. By analyzing the individual contribution of the basis signals, one can infer properties of the original signal. This dissertation presents an overview of the extension of the classical signal processing theory to graph domains. Specifically, we review the graph Fourier transform and graph wavelet transforms both of which based on the spectral graph theory, and explore their properties through illustrative examples. The main features of the spect
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Siameh, Theophilus. "Graph Analytics Methods In Feature Engineering." Digital Commons @ East Tennessee State University, 2017. https://dc.etsu.edu/etd/3307.

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High-dimensional data sets can be difficult to visualize and analyze, while data in low-dimensional space tend to be more accessible. In order to aid visualization of the underlying structure of a dataset, the dimension of the dataset is reduced. The simplest approach to accomplish this task of dimensionality reduction is by a random projection of the data. Even though this approach allows some degree of visualization of the underlying structure, it is possible to lose more interesting underlying structure within the data. In order to address this concern, various supervised and unsupervised l
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Priya, Rashmi. "RETAIL DATA ANALYTICS USING GRAPH DATABASE." UKnowledge, 2018. https://uknowledge.uky.edu/cs_etds/67.

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Big data is an area focused on storing, processing and visualizing huge amount of data. Today data is growing faster than ever before. We need to find the right tools and applications and build an environment that can help us to obtain valuable insights from the data. Retail is one of the domains that collects huge amount of transaction data everyday. Retailers need to understand their customer’s purchasing pattern and behavior in order to take better business decisions. Market basket analysis is a field in data mining, that is focused on discovering patterns in retail’s transaction data. Our
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Papadopoulos, Stavros. "Novel graph analytics for enhancing data insight." Thesis, Imperial College London, 2015. http://hdl.handle.net/10044/1/42992.

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Graph analytics is a fast growing and significant field in the visualization and data mining community, which is applied on numerous high-impact applications such as, network security, finance, and health care, providing users with adequate knowledge across various patterns within a given system. Although a series of methods have been developed in the past years for the analysis of unstructured collections of multi-dimensional points, graph analytics has only recently been explored. Despite the significant progress that has been achieved recently, there are still many open issues in the area,
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Alsahafy, Maram Saad M. "Efficient Algorithms for Speeding Up Graph Data Analytics." Thesis, The University of Sydney, 2021. https://hdl.handle.net/2123/26166.

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Graph model has been playing an important role in analyzing the data from real applications such as social networks, communication networks, and information networks. It models entities of the applications as vertices/nodes in the graph, and models relationships among the entities as edges between vertices in the graph. In recent years there has been an increasing number of studies of complex graph analysis coinciding with the rapid development of information technologies, such as online social networks and (mobile/email) communication networks. Due to the growing sizes of these graph data,
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Sun, Jiawen. "The GraphGrind framework : fast graph analytics on large shared-memory systems." Thesis, Queen's University Belfast, 2018. https://pure.qub.ac.uk/portal/en/theses/the-graphgrind-framework-fast-graph-analytics-on-large-sharedmemory-systems(e1eb006f-3a68-4d05-91fe-961d04b42694).html.

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As shared memory systems support terabyte-sized main memory, they provide an opportunity to perform efficient graph analytics on a single machine. Graph analytics is characterised by frequent synchronisation, which is addressed in part by shared memory systems. However, performance is limited by load imbalance and poor memory locality, which originate in the irregular structure of small-world graphs. This dissertation demonstrates how graph partitioning can be used to optimise (i) load balance, (ii) Non-Uniform Memory Access (NUMA) locality and (iii) temporal locality of graph partitioning in
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Yang, Xintian. "Towards large-scale network analytics." The Ohio State University, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=osu1343680930.

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Huang, Xiaoke. "USING GRAPH MODELING IN SEVERAL VISUAL ANALYTIC TASKS." Kent State University / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=kent1467738860.

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Heibi, Ivan. "A Visual Framework for Graph and Text Analytics in Email Investigation." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017. http://amslaurea.unibo.it/13818/.

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The aim of this work is to build a framework which can benefit from data analysis techniques to explore and mine important information stored in an email collection archive. The analysis of email data could be accomplished from different perspectives, we mainly focused our approach on two different aspects: social behaviors and the textual content of the emails body. We will present a review on the past techniques and features adopted to handle this type of analysis, and evaluate them in real tools. This background will motivate our choices and proposed approach, and help us build a final visu
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Books on the topic "Graph Analytics"

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Bader, David A. Massive Graph Analytics. Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003033707.

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Cheramangalath, Unnikrishnan, Rupesh Nasre, and Y. N. Srikant. Distributed Graph Analytics. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-41886-1.

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Gunturi, Venkata M. V., and Shashi Shekhar. Spatio-Temporal Graph Data Analytics. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67771-2.

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Yan, Da, Yuanyuan Tian, and James Cheng. Systems for Big Graph Analytics. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-58217-7.

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Martella, Claudio, Roman Shaposhnik, and Dionysios Logothetis. Practical Graph Analytics with Apache Giraph. Apress, 2015. http://dx.doi.org/10.1007/978-1-4842-1251-6.

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Qin, Lu, Wenjie Zhang, Ying Zhang, et al., eds. Software Foundations for Data Interoperability and Large Scale Graph Data Analytics. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-61133-0.

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Leithold, Louis. Before calculus: Functions, graphs, and analyticgeometry. 3rd ed. HarperCollins College Publishers, 1994.

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Leithold, Louis. Before calculus: Functions, graphs, and analytic geometry. Harper & Row, 1985.

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Isakov, Vladimir. Speak the language of schemes. INFRA-M Academic Publishing LLC., 2022. http://dx.doi.org/10.12737/1860649.

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Schematization and visualization are the necessary means to ensure the activity of a modern specialist. Schematization allows you to highlight the main thing in an object, to discover its constituent elements, to show their relationship, gives impetus to the construction of conceptual approaches. Visualization "dresses" schematic concepts in a bright, expressive artistic and graphic form. The handbook provides descriptions of the most popular means of analytical graphics - maps, graphs, tables, graphs, diagrams, flowcharts (algorithms), chronolents, maps, methodological schemes, etc. The ways
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Ishiwata, Satoshi. Geometric and analytic properties in the behavior of random walks on nilpotent covering graphs. Tohoku University, 2004.

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Book chapters on the topic "Graph Analytics"

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Cheramangalath, Unnikrishnan, Rupesh Nasre, and Y. N. Srikant. "Graph Analytics Frameworks." In Distributed Graph Analytics. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-41886-1_4.

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Rahman, Ahsanur, and Tamanna Motahar. "Big Graph Analytics." In Data Analytics. CRC Press, 2018. http://dx.doi.org/10.1201/9780429446177-5.

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Srikant, Y. N. "Distributed Graph Analytics." In Distributed Computing and Internet Technology. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-36987-3_1.

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Cheramangalath, Unnikrishnan, Rupesh Nasre, and Y. N. Srikant. "Dynamic Graph Algorithms." In Distributed Graph Analytics. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-41886-1_6.

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Cheramangalath, Unnikrishnan, Rupesh Nasre, and Y. N. Srikant. "Introduction to Graph Analytics." In Distributed Graph Analytics. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-41886-1_1.

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Cheramangalath, Unnikrishnan, Rupesh Nasre, and Y. N. Srikant. "Graph Algorithms and Applications." In Distributed Graph Analytics. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-41886-1_2.

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Cheramangalath, Unnikrishnan, Rupesh Nasre, and Y. N. Srikant. "Efficient Parallel Implementation of Graph Algorithms." In Distributed Graph Analytics. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-41886-1_3.

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Cheramangalath, Unnikrishnan, Rupesh Nasre, and Y. N. Srikant. "GPU Architecture and Programming Challenges." In Distributed Graph Analytics. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-41886-1_5.

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Cheramangalath, Unnikrishnan, Rupesh Nasre, and Y. N. Srikant. "Falcon: A Domain Specific Language for Graph Analytics." In Distributed Graph Analytics. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-41886-1_7.

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Cheramangalath, Unnikrishnan, Rupesh Nasre, and Y. N. Srikant. "Experiments, Evaluation and Future Directions." In Distributed Graph Analytics. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-41886-1_8.

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Conference papers on the topic "Graph Analytics"

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Yang, Min Jian, Yueling Jenny Zeng, and Li-C. Wang. "WM-Graph: Graph-Based Approach for Wafermap Analytics." In 2024 IEEE International Test Conference (ITC). IEEE, 2024. http://dx.doi.org/10.1109/itc51657.2024.00030.

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Cuzzocrea, Alfredo, and Il-Yeol Song. "Big Graph Analytics." In the 17th International Workshop. ACM Press, 2014. http://dx.doi.org/10.1145/2666158.2668454.

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Yan, Da, Yingyi Bu, Yuanyuan Tian, Amol Deshpande, and James Cheng. "Big Graph Analytics Systems." In SIGMOD/PODS'16: International Conference on Management of Data. ACM, 2016. http://dx.doi.org/10.1145/2882903.2912566.

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Xia, Yinglong, Ilie Gabriel Tanase, Lifeng Nai, et al. "Graph analytics and storage." In 2014 IEEE International Conference on Big Data (Big Data). IEEE, 2014. http://dx.doi.org/10.1109/bigdata.2014.7004326.

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Langguth, Johannes. "PGAS for graph analytics." In CF '19: Computing Frontiers Conference. ACM, 2019. http://dx.doi.org/10.1145/3310273.3324293.

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Nagpurkar, Priya. "Session details: Graph analytics." In PPoPP '14: ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming. ACM, 2014. http://dx.doi.org/10.1145/3255635.

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Pan, Yuechao, Yangzihao Wang, Yuduo Wu, Carl Yang, and John D. Owens. "Multi-GPU Graph Analytics." In 2017 IEEE International Parallel and Distributed Processing Symposium (IPDPS). IEEE, 2017. http://dx.doi.org/10.1109/ipdps.2017.117.

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da Trindade, Joana M. F., Konstantinos Karanasos, Carlo Curino, Samuel Madden, and Julian Shun. "Kaskade: Graph Views for Efficient Graph Analytics." In 2020 IEEE 36th International Conference on Data Engineering (ICDE). IEEE, 2020. http://dx.doi.org/10.1109/icde48307.2020.00024.

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Zahka, Daniel, and Ada Gavrilovska. "FAM-Graph: Graph Analytics on Disaggregated Memory." In 2022 IEEE International Parallel and Distributed Processing Symposium (IPDPS). IEEE, 2022. http://dx.doi.org/10.1109/ipdps53621.2022.00017.

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Rozanec, Joze, Michael Cochez, Ruud van Bakel, Brian Elvesæter, and Dumitru Roman. "Graph-Scrutinizer: Towards Massive Graph Analytics and Reasoning." In ICPE '23: ACM/SPEC International Conference on Performance Engineering. ACM, 2023. http://dx.doi.org/10.1145/3578245.3585338.

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Reports on the topic "Graph Analytics"

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Tennant, C., T. Larrieu, D. Moser, J. Li, S. Want, and Z. Xu. Graph Analytics for CEBAF Operations. Office of Scientific and Technical Information (OSTI), 2024. http://dx.doi.org/10.2172/2331262.

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Hong, Seokyong, Sangkeun Lee, Seung Hwan Lim, Sreenivas R. Sukumar, and Ranga Raju Vatsavai. Query optimization for graph analytics on linked data using SPARQL. Office of Scientific and Technical Information (OSTI), 2015. http://dx.doi.org/10.2172/1215587.

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Bernstetter, Armin. The Digital Lab Book (software). GEOMAR Helmholtz Centre for Ocean Research Kiel, 2024. http://dx.doi.org/10.3289/sw_7_2024.

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The Digital Lab Book is a provenance visualization and management tool for immersive and collaborative cisual analytics. It acts as a direct companion to the Virtual Fieldwork Unreal Engine application providing a web-application that tracks interaction in the Unreal Engine. Each interaction in Unreal Engine triggers committing a visualization state to the DLB. The interaction history can be managed similar to a git repository by creating branches and stepping back to previous states in a visualized repository graph. The visualization states can be annotated, and organized in an internal mind-
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Hrebeniuk, Bohdan V. Modification of the analytical gamma-algorithm for the flat layout of the graph. [б. в.], 2018. http://dx.doi.org/10.31812/123456789/2882.

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The planarity of graphs is one of the key sections of graph theory. Although a graph is an abstract mathematical object, most often it is graph visualization that makes it easier to study or develop in a particular area, for example, the infrastructure of a city, a company’s management or a website’s web page. In general, in the form of a graph, it is possible to depict any structures that have connections between the elements. But often such structures grow to such dimensions that it is difficult to determine whether it is possible to represent them on a plane without intersecting the bonds.
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Kupina, Steve, Mark Kelm, Maria Monagas, and STEFAN GAFNER. Grape Seed Extract Laboratory Guidance Document. ABC-AHP-NCNPR Botanical Adulterants Prevention Program, 2019. http://dx.doi.org/10.59520/bapp.lgd/dozo2637.

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Grape Seed Extract (GSE) has received acceptance almost globally as an ingredient for human consumption. It is one of the more widely used botanical extracts, due to increasing scientific findings supporting health benefits. However, it remains a specialty item relative to global commodities. In the United States, GSE has ranked among the top 20 best-selling dietary supplements in the Food, Drug and Mass Market channel. The motivation behind purposeful adulteration in commercial products is financial gain (also known as economically motivated adulteration) and to increase the concentration in
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