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Journal articles on the topic 'Graph Analytics'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

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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Du, Zhihui, Oliver Alvarado Rodriguez, Joseph Patchett, and David A. Bader. "Interactive Graph Stream Analytics in Arkouda." Algorithms 14, no. 8 (2021): 221. http://dx.doi.org/10.3390/a14080221.

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Data from emerging applications, such as cybersecurity and social networking, can be abstracted as graphs whose edges are updated sequentially in the form of a stream. The challenging problem of interactive graph stream analytics is the quick response of the queries on terabyte and beyond graph stream data from end users. In this paper, a succinct and efficient double index data structure is designed to build the sketch of a graph stream to meet general queries. A single pass stream model, which includes general sketch building, distributed sketch based analysis algorithms and regression based

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Zhang, Yuhao, and Arun Kumar. "Lotan: Bridging the Gap between GNNs and Scalable Graph Analytics Engines." Proceedings of the VLDB Endowment 16, no. 11 (2023): 2728–41. http://dx.doi.org/10.14778/3611479.3611483.

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Recent advances in Graph Neural Networks (GNNs) have changed the landscape of modern graph analytics. The complexity of GNN training and the scalability challenges have also sparked interest from the systems community, with efforts to build systems that provide higher efficiency and schemes to reduce costs. However, we observe that many such systems basically "reinvent the wheel" of much work done in the database world on scalable graph analytics engines. Further, they often tightly couple the scalability treatments of graph data processing with that of GNN training, resulting in entangled com

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Hajidehi, Milad Rezaei, Sraavan Sridhar, and Margo Seltzer. "CUTTANA: Scalable Graph Partitioning for Faster Distributed Graph Databases and Analytics." Proceedings of the VLDB Endowment 18, no. 1 (2024): 14–27. https://doi.org/10.14778/3696435.3696437.

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Graph partitioning plays a pivotal role in various distributed graph processing applications, including graph analytics, graph neural network training, and distributed graph databases. A "good" graph partitioner reduces workload execution time, worker imbalance, and network overhead. Graphs that require distributed settings are often too large to fit in the main memory of a single machine. This challenge renders traditional in-memory graph partitioners infeasible, leading to the emergence of streaming solutions. Streaming partitioners produce lower-quality partitions, because they work from pa

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Yan, Da, Yingyi Bu, Yuanyuan Tian, and Amol Deshpande. "Big Graph Analytics Platforms." Foundations and Trends® in Databases 7, no. 1-2 (2017): 1–195. http://dx.doi.org/10.1561/1900000056.

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Chou, Chung-Hsien, Masahiro Hayakawa, Atsushi Kitazawa, and Phillip Sheu. "GOLAP: Graph-Based Online Analytical Processing." International Journal of Semantic Computing 12, no. 04 (2018): 595–608. http://dx.doi.org/10.1142/s1793351x18500071.

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Graph-based Online Analytical Processing (GOLAP) extends Online Analytical Processing (OLAP) to address graph-based problems that involve object attributes. Based on graph data, GOLAP can answer user queries related to combinatorial optimization, structural analytics, and influence analytics. Besides, since a GOLAP system is an online interactive system that requires fast response time, the execution time for graph-problem queries is essentially critical. Thus, how to speed up the execution time of specific graph problems becomes a challenge in GOLAP. In this paper, we show several methods to

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Jin, Jennifer, and Masahiro Hayakawa. "Network analysis and GOLAP." Encyclopedia with Semantic Computing and Robotic Intelligence 02, no. 02 (2018): 1850016. http://dx.doi.org/10.1142/s2529737618500168.

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Online Analytical Processing (OLAP) is an effective approach to analyzing various complex business problems, and graph is considered as a common scheme to represent the business datasets. Network analysis is a broad analytics scheme for exploring the connectivity and deriving useful analytics results. However, network analysis for graph-based OLAP presents a set of more specific analytics methods by utilizing graph model, network property, and OLAP principles. In this paper, we present a comprehensive survey on network analysis conducted on graph model for the purpose of OLAP, and we summarize

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17

Burkhardt, Paul. "Graphing trillions of triangles." Information Visualization 16, no. 3 (2016): 157–66. http://dx.doi.org/10.1177/1473871616666393.

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The increasing size of Big Data is often heralded but how data are transformed and represented is also profoundly important to knowledge discovery, and this is exemplified in Big Graph analytics. Much attention has been placed on the scale of the input graph but the product of a graph algorithm can be many times larger than the input. This is true for many graph problems, such as listing all triangles in a graph. Enabling scalable graph exploration for Big Graphs requires new approaches to algorithms, architectures, and visual analytics. A brief tutorial is given to aid the argument for though

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18

Natarajan, Maya. "How Knowledge Graphs are Changing Data Analytics." ITNOW 64, no. 1 (2022): 52–53. http://dx.doi.org/10.1093/itnow/bwac027.

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19

Xu, Qian, Juan Yang, Feng Zhang, et al. "Improving Graph Compression for Efficient Resource-Constrained Graph Analytics." Proceedings of the VLDB Endowment 17, no. 9 (2024): 2212–26. http://dx.doi.org/10.14778/3665844.3665852.

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Recent studies have shown the promise of directly processing compressed graphs. However, its benefits have been limited by high peak-memory usage and unbearably long compression time. In this paper, we introduce Laconic, a novel rule-based graph processing solution that overcomes the challenges of restricted memory and impractical compression time faced by existing approaches. Laconic, for the first time, ensures minimal memory overhead during compression and significantly reduces graph sizes, thus reducing peak memory demand during computations. By employing an efficient parallel compression

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20

Fuchs, Per, Domagoj Margan, and Jana Giceva. "Sortledton: a Universal Graph Data Structure." ACM SIGMOD Record 52, no. 1 (2023): 17–25. http://dx.doi.org/10.1145/3604437.3604442.

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Despite the wide adoption of graph processing across many different application domains, there is no underlying data structure that can serve a variety of graph workloads (analytics, traversals, and pattern matching) on dynamic graphs with single edge updates updates.

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21

Hoang, Loc, Roshan Dathathri, Gurbinder Gill, and Keshav Pingali. "CuSP." ACM SIGOPS Operating Systems Review 55, no. 1 (2021): 47–60. http://dx.doi.org/10.1145/3469379.3469385.

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Graph analytics systems must analyze graphs with billions of vertices and edges which require several terabytes of storage. Distributed-memory clusters are often used for analyzing such large graphs since the main memory of a single machine is usually restricted to a few hundreds of gigabytes. This requires partitioning the graph among the machines in the cluster. Existing graph analytics systems use a built-in partitioner that incorporates a particular partitioning policy, but the best policy is dependent on the algorithm, input graph, and platform. Therefore, built-in partitioners are not su

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22

Zhang, Fangyan, Song Zhang, and Pak Chung Wong. "Graph Sampling for Visual Analytics." Journal of Imaging Science and Technology 61, no. 4 (2017): 405031–4050311. http://dx.doi.org/10.2352/j.imagingsci.technol.2017.61.4.040503.

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23

Pak Chung Wong, H. Foote, G. Chin, P. Mackey, and K. Perrine. "Graph Signatures for Visual Analytics." IEEE Transactions on Visualization and Computer Graphics 12, no. 6 (2006): 1399–413. http://dx.doi.org/10.1109/tvcg.2006.92.

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24

Zhang, Kaiyuan, Rong Chen, and Haibo Chen. "NUMA-aware graph-structured analytics." ACM SIGPLAN Notices 50, no. 8 (2015): 183–93. http://dx.doi.org/10.1145/2858788.2688507.

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25

Liu, Yushi, Liwei Yuan, Zhihao Chen, et al. "ChainDash: An Ad-Hoc Blockchain Data Analytics System." Proceedings of the VLDB Endowment 16, no. 12 (2023): 4022–25. http://dx.doi.org/10.14778/3611540.3611611.

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The emergence of digital asset applications, driven by Web 3.0 and powered by blockchain technology, has led to a growing demand for blockchain-specific graph analytics to unearth the insights. However, current blockchain data analytics systems are unable to perform efficient ad-hoc graph analytics over both live and past time windows due to their inefficient data synchronization and slow graph snapshots retrieval capability. To address these issues, we propose ChainDash, a blockchain data analytics system that dedicates a highly-parallelized data synchronization component and a retrieval-opti

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Shekhar Agrawal and Rahul Vats. "Dynamic Knowledge Graphs: Revolutionizing Skill Analytics through Graph Neural Networks." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 1 (2025): 1838–48. https://doi.org/10.32628/cseit251112181.

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The dynamic nature of workforce skills and their interrelationships necessitates sophisticated systems for understanding, classifying, and predicting skill evolution. This article introduces a novel framework for Dynamic Knowledge Graph Evolution in the Skills Domain, leveraging Graph Neural Networks to model hierarchical skill relationships, analyze temporal trends, and automate taxonomy generation. Our architecture incorporates a hierarchical GNN model that captures parent-child relationships among skills, enabling accurate skill classification and clustering. Temporal article is integrated

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Chen, Zheng, Feng Zhang, Yang Chen, et al. "Enabling Window-Based Monotonic Graph Analytics with Reusable Transitional Results for Pattern-Consistent Queries." Proceedings of the VLDB Endowment 17, no. 11 (2024): 3003–16. http://dx.doi.org/10.14778/3681954.3681979.

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Evolving graphs consisting of slices are large and constantly changing. For example, in Alipay, the graph generates hundreds of millions of new transaction records every day. Analyzing the graph within a temporary window is time-consuming due to the heavy merging of slices. Fortunately, we have discovered that most queries exhibit consistent patterns and possess monotonic properties. As a result, transitional results can be computed within slice generation for reuse. Accordingly, we develop MergeGraph enabling window-based monotonic graph analytics with reusable transitional results for patter

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Jin, Hai, Hao Qi, Jin Zhao, et al. "Software Systems Implementation and Domain-Specific Architectures towards Graph Analytics." Intelligent Computing 2022 (October 29, 2022): 1–32. http://dx.doi.org/10.34133/2022/9806758.

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Graph analytics, which mainly includes graph processing, graph mining, and graph learning, has become increasingly important in several domains, including social network analysis, bioinformatics, and machine learning. However, graph analytics applications suffer from poor locality, limited bandwidth, and low parallelism owing to the irregular sparse structure, explosive growth, and dependencies of graph data. To address those challenges, several programming models, execution modes, and messaging strategies are proposed to improve the utilization of traditional hardware and performance. In rece

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Nolé, Maurizio, and Carlo Sartiani. "Graph Management Systems: A Qualitative Survey." APTIKOM Journal on Computer Science and Information Technologies 5, no. 1 (2020): 37–49. http://dx.doi.org/10.34306/csit.v5i1.132.

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In the recent years many real-world applications have been modeled by graph structures (e.g., social networks, mobile phone networks, web graphs, etc.), and many systems have been developed to manage, query, and analyze these datasets. These systems could be divided into specialized graph database systems and large-scale graph analytics systems. The first ones consider end-to-end data management issues including storage representations, transactions, and query languages, whereas the second ones focus on processing specific tasks over large data graphs. In this paper we provide an overview of s

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Nolé, Maurizio, and Carlo Sartiani. "Graph Management Systems: A Qualitative Survey." APTIKOM Journal on Computer Science and Information Technologies 3, no. 2 (2018): 66–76. http://dx.doi.org/10.11591/aptikom.j.csit.129.

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In the recent years many real-world applications have been modeled by graph structures (e.g., social networks, mobile phone networks, web graphs, etc.), and many systems have been developed to manage, query, and analyze these datasets. These systems could be divided into specialized graph database systems and large-scale graph analytics systems. The first ones consider end-to-end data management issues including storage representations, transactions, and query languages, whereas the second ones focus on processing specific tasks over large data graphs. In this paper we provide an overview of s

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31

Ye, Nanjun. "A Penetrative Multidimensional Data Analytics Model for Complex Relationship Mining over Knowledge Graphs." Journal of Computing and Electronic Information Management 17, no. 2 (2025): 34–41. https://doi.org/10.54097/87rgwp44.

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This study proposes a deep multidimensional data analytics framework for extracting intricate relationships from knowledge graphs, which tackles the challenge of discovering hidden connections in heterogeneous and high-dimensional datasets. The proposed method unifies three principal elements: Dynamic Meta-Path Penetration, Nested Subgraph Extraction, and Tensor-Graph Fusion, which together permit a structured investigation of hidden connections. Dynamic Meta-Path Penetration applies reinforcement learning to traverse the graph, directed by a reward system prioritizing informative routes. Nest

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32

Fuchs, Per, Domagoj Margan, and Jana Giceva. "Sortledton." Proceedings of the VLDB Endowment 15, no. 6 (2022): 1173–86. http://dx.doi.org/10.14778/3514061.3514065.

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Despite the wide adoption of graph processing across many different application domains, there is no underlying data structure that can serve a variety of graph workloads (analytics, traversals, and pattern matching) on dynamic graphs with transactional updates. In this paper, we present Sortledton, a universal graph data structure that addresses the open problem by being carefully optimizing for the most relevant data access patterns used by graph computation kernels. It can support millions of transactional updates per second, while providing competitive performance (1.22x on average) for th

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33

Shi, Jifan, Biao Wang, and Yun Xu. "Spruce: a Fast yet Space-saving Structure for Dynamic Graph Storage." Proceedings of the ACM on Management of Data 2, no. 1 (2024): 1–26. http://dx.doi.org/10.1145/3639282.

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Dynamic graphs have been gaining increasing popularity across various application domains. With the growing size of these graphs, the update performance as well as space occupancy is becoming a crucial aspect of dynamic graph storage. Although existing dynamic graph systems can handle massive streaming updates (e.g., insertions and deletions), they cannot achieve both high throughput and low memory footprint. Drawing inspiration from the basic operations of the van Emde Boas (vEB) tree in double-logarithmic time, we designed Spruce, a high-performance yet space-saving in-memory structure to st

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34

Kennedy, Andrew, Karsten Klein, An Nguyen, and Florence Ying Wang. "The Graph Landscape: using visual analytics for graph set analysis." Journal of Visualization 20, no. 3 (2016): 417–32. http://dx.doi.org/10.1007/s12650-016-0374-6.

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35

Hasan, S. M. Shamimul, Donna Rivera, Xiao-Cheng Wu, Eric B. Durbin, J. Blair Christian, and Georgia Tourassi. "Knowledge Graph-Enabled Cancer Data Analytics." IEEE Journal of Biomedical and Health Informatics 24, no. 7 (2020): 1952–67. http://dx.doi.org/10.1109/jbhi.2020.2990797.

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Sha, Mo, Yuchen Li, Bingsheng He, and Kian-Lee Tan. "Accelerating dynamic graph analytics on GPUs." Proceedings of the VLDB Endowment 11, no. 1 (2017): 107–20. http://dx.doi.org/10.14778/3151113.3151122.

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37

Prakash, Saurav, Amirhossein Reisizadeh, Ramtin Pedarsani, and Amir Salman Avestimehr. "Coded Computing for Distributed Graph Analytics." IEEE Transactions on Information Theory 66, no. 10 (2020): 6534–54. http://dx.doi.org/10.1109/tit.2020.2999675.

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38

Ma, Kwan-Liu, and Chris W. Muelder. "Large-Scale Graph Visualization and Analytics." Computer 46, no. 7 (2013): 39–46. http://dx.doi.org/10.1109/mc.2013.242.

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Xing, Yuxuan, Zhiguang Chen, Nong Xiao, Fang Liu, and Yutong Lu. "Graph Analytics on Manycore Memory Systems." IEEE Access 6 (2018): 51429–39. http://dx.doi.org/10.1109/access.2018.2869463.

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40

Ozdal, Muhammet Mustafa, Serif Yesil, Taemin Kim, et al. "Graph Analytics Accelerators for Cognitive Systems." IEEE Micro 37, no. 1 (2017): 42–51. http://dx.doi.org/10.1109/mm.2017.7.

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Rabzelj, Matej, Ciril Bohak, Leon Štefanič Južnič, Andrej Kos, and Urban Sedlar. "Cyberattack Graph Modeling for Visual Analytics." IEEE Access 11 (2023): 86910–44. http://dx.doi.org/10.1109/access.2023.3304640.

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Wong, Pak Chung, Harlan Foote, Patrick Mackey, George Chin, Heidi Sofia, and Jim Thomas. "A Dynamic Multiscale Magnifying Tool for Exploring Large Sparse Graphs." Information Visualization 7, no. 2 (2008): 105–17. http://dx.doi.org/10.1057/palgrave.ivs.9500177.

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We present an information visualization tool, known as GreenMax, to visually explore large small-world graphs with up to a million graph nodes on a desktop computer. A major motivation for scanning a small-world graph in such a dynamic fashion is the demanding goal of identifying not just the well-known features but also the unknown–known and unknown–unknown features of the graph. GreenMax uses a highly effective multilevel graph drawing approach to pre-process a large graph by generating a hierarchy of increasingly coarse layouts that later support the dynamic zooming of the graph. This paper

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Zhang, Hui, Yingtao Niu, Kun Ding, Shaoqin Kou, and Liu Liu. "Building and Applying Knowledge Graph in Edge Analytics Environment." Journal of Physics: Conference Series 2171, no. 1 (2022): 012014. http://dx.doi.org/10.1088/1742-6596/2171/1/012014.

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Abstract For the scenario of limited hardware resources and restricted software environment in edge computing architecture, the method of building and applying knowledge graph in edge analytics environment is proposed. The main process includes: building knowledge graph in the cloud, storing knowledge base with RDF format at the edge through customization and transformation, and performing query and analytics at the edge with SPARQL graph search language. The method is simulated in a communication anti-jamming test environment, and the results show that the relevant technical solutions can bet

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Ahmed, Nesreen, and Ryan Rossi. "Interactive Visual Graph Analytics on the Web." Proceedings of the International AAAI Conference on Web and Social Media 9, no. 1 (2021): 566–69. http://dx.doi.org/10.1609/icwsm.v9i1.14653.

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We present a web-based network visual analytics platform called GraphVis that combines interactive visualizations with analytic techniques to reveal important patterns and insights for sense making, reasoning, and decision-making. The platform is designed with simplicity in mind and allows users to visualize and explore networks in seconds with a simple drag-and-drop of a graph file into the web browser. GraphVis is fast and flexible, web-based, requires no installation, while supporting a wide range of graph formats as well as state-of-the-art visualization and analytic techniques. In particu

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Anadiotis, Angelos Christos, Muhammad Ghufran Khan, and Ioana Manolescu. "Dynamic Graph Databases with Out-of-Order Updates." Proceedings of the VLDB Endowment 17, no. 13 (2024): 4799–812. https://doi.org/10.14778/3704965.3704984.

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Several real-time applications rely on dynamic graphs to model and store data arriving from multiple streams. Providing both high ingestion rate and efficient analytics with transactional guarantees is challenging, even more so when updates may be received out-of-order at the database. In this work, we propose HAL, a novel in-memory dynamic graph database design, addressing these challenges. HAL outperforms comparable systems by a factor of up to 73× in terms of update processing throughput and up to 357× for analytics, while being the first to support out-of-order updates.

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El Moussawi, Adnan, Nacera Bennacer Seghouani, and Francesca Bugiotti. "BGRAP: Balanced GRAph Partitioning Algorithm for Large Graphs." Journal of Data Intelligence 2, no. 2 (2021): 116–35. http://dx.doi.org/10.26421/jdi2.2-2.

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The definition of effective strategies for graph partitioning is a major challenge in distributed environments since an effective graph partitioning allows to considerably improve the performance of large graph data analytics computations. In this paper, we propose a multi-objective and scalable Balanced GRAph Partitioning (\algo) algorithm, based on Label Propagation (LP) approach, to produce balanced graph partitions. \algo defines a new efficient initialization procedure and different objective functions to deal with either vertex or edge balance constraints while considering edge direction

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Papon, Tarikul Islam, Taishan Chen, Shuo Zhang, and Manos Athanassoulis. "CAVE: Concurrency-Aware Graph Processing on SSDs." Proceedings of the ACM on Management of Data 2, no. 3 (2024): 1–26. http://dx.doi.org/10.1145/3654928.

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Large-scale graph analytics has become increasingly common in areas like social networks, physical sciences, transportation networks, and recommendation systems. Since many such practical graphs do not fit in main memory, graph analytics performance depends on efficiently utilizing underlying storage devices. These out-of-core graph processing systems employ sharding and sub-graph partitioning to optimize for storage while relying on efficient sequential access of traditional hard disks. However, today's storage is increasingly based on solid-state drives (SSDs) that exhibit high internal para

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Prasad, Rudrapati Bhuvaneswara, and Avutala Mallikarjuna Reddy. "Edge properties of lexicographic product graphs of open neighborhood graphs." Scientific Temper 16, no. 01 (2025): 3664–73. https://doi.org/10.58414/scientifictemper.2025.16.1.12.

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This research investigates the complex edge characteristics of lexicographic product graphs formed from open neighborhood graphs, filling a notable gap in understanding their structural and adjacency features. Such graphs are pivotal in combinatorial optimization, network architecture, and computational graph theory, particularly for analyzing large-scale systems. By employing rigorous mathematical formulations, the study calculates vertex degrees, edge counts, and degree regularity across diverse graph classes, including cycles, complete graphs, and bipartite structures. A key discovery is th

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Yang, Haoduo, Huayou Su, Qiang Lan, Mei Wen, and Chunyuan Zhang. "HPGraph: High-Performance Graph Analytics with Productivity on the GPU." Scientific Programming 2018 (December 11, 2018): 1–11. http://dx.doi.org/10.1155/2018/9340697.

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The growing use of graph in many fields has sparked a broad interest in developing high-level graph analytics programs. Existing GPU implementations have limited performance with compromising on productivity. HPGraph, our high-performance bulk-synchronous graph analytics framework based on the GPU, provides an abstraction focused on mapping vertex programs to generalized sparse matrix operations on GPU as the backend. HPGraph strikes a balance between performance and productivity by coupling high-performance GPU computing primitives and optimization strategies with a high-level programming mod

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Liu, Yang, Wenfei Fan, Shuhao Liu, Xiaoke Zhu, and Jianxin Li. "A Single Machine System for Querying Big Graphs with PRAM." Proceedings of the VLDB Endowment 18, no. 3 (2024): 756–69. https://doi.org/10.14778/3712221.3712240.

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This paper develops Planar (Plug and play PRAM), a single-machine system for graph analytics by reusing existing PRAM algorithms, without the need for designing new parallel algorithms. Planar supports both out-of-core and in-memory analytics. When a graph is too big to fit into the memory of a machine, Planar adapts PRAM to limited resources by extending a fixpoint model with multi-core parallelism, using disk as memory extension. For an in-memory task, it dedicates all available CPU cores to the task, and allows parallelly scalable PRAM algorithms to retain the property, i.e. , the more core

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