[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-83737-en":3,"doc-seo-83737-105":29,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},83737,549758252649,"Ivy","https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819",8,"Research & Report","Task-Centered Benchmark for Interactive Network Visualization and Analysis","Interactive network visualization and analysis (INVA) supports iterative visual and algorithmic exploration of large network datasets. Existing benchmarks evaluate graph algorithms and systems but provide limited coverage for interactive network understanding with human-in-the-loop workflows. This work builds the first task-centered benchmarking framework to assess diverse graph system backends on INVA workloads, highlighting capability and performance gaps and exposing correctness issues, while outlining opportunities for improving interactive network tooling.","Task-Centered Benchmark for Interactive Network Visualization  \n& Analysis  \nExperiments & Analysis  \nAmeya Patil University of Washington  \nSeattle, USA [ameyap2@cs.washington.edu](ameyap2@cs.washington.edu)  \nWei Jun Tan University of Washington  \nSeattle, USA[wj428@cs.washington.edu](wj428@cs.washington.edu)  \narXiv :2607 .03725v 1 [ cs .DB] 4 Jul 2026  \nIshan Sinha University of Washington  \nSeattle, USA [ishans2@cs.washington.edu](ishans2@cs.washington.edu)  \nLeilani Battle University of Washington  \nSeattle, USA [leibatt@cs.washington.edu](leibatt@cs.washington.edu)  \nABSTRACT  \nInteractive network visualization and analysis (INVA) enables iterative, visual and algorithmic analysis of large network datasets. Although numerous benchmarks have been developed to evaluate different graph analysis algorithms and systems, we observe a lack of such efforts for interactive network data understanding. In this work, we address the question-How well do existing graph systems serve the purpose of Interactive Network Visualization and Analysis? To this end, we build and demonstrate the use of the first task-centered benchmarking framework to evaluate a variety of graph system backends on INVA workloads. Our benchmarking results highlight a gap between both the capabilities and performance of existing graph systems for INVA use cases, and uncover possible bugs in these systems. Based on our benchmarking results, we reveal new opportunities for research and development to better support interactive network visualization and analysis.  \nPVLDB Reference Format:  \nAmeya Patil, Wei Jun Tan, Ishan Sinha, and Leilani Battle. Task-Centered Benchmark for Interactive Network Visualization & Analysis. PVLDB, 14(1): XXX-XXX, 2020 .  \ndoi:XX.XX/XXX.XX  \nPVLDB Artifact Availability:  \nThe source code, data, and/or other artifacts have been made available at [https://github.com/WeiJun428/graph-system-benchmark and](https://github.com/WeiJun428/graph-system-benchmark and)  \n[https://osf.io/3z4hk](https://osf.io/3z4hk)  \n1 INTRODUCTION  \nThe ever-growing importance of large-scale network data has sparked increased interest in visualizing and analyzing large networks [10] . In response, many graph algorithms [36, 51, 59, 63], graph processing frameworks and systems [32, 33, 53, 65] have been developed  \nThis work is licensed under the Creative Commons BY-NC-ND 4.0 International License. Visit [https://creativecommons.org/licenses/by-nc-nd/4.0/ to view a copy of](https://creativecommons.org/licenses/by-nc-nd/4.0/ to view a copy of)[ ](https://creativecommons.org/licenses/by-nc-nd/4.0/ to view a copy of)[this license. For any use beyond those covered by this license](this license. For any use beyond those covered by this license), [obtain permission by](obtain permission by)[emailing info@vldb.org. Copyright](emailing info@vldb.org. Copyright) is held by the owner/author(s). Publication rights licensed to the VLDB Endowment.  \nProceedings of the VLDB Endowment, Vol. 14, No. 1 ISSN 2150-8097 .  \ndoi:XX.XX/XXX.XX  \nto support large graph analytics. However, these solutions are predominantly opaque to domain experts or non-programmers, and fail to account for human-in-the-loop or interactive understanding of network data.  \n In this work,  we focus on a relatively understudied aspect of network data understanding: interactive network visualization and analysis (INVA). INVA facilitates quick exploration and sensemaking of large complex network datasets including critical tasks across the network analysis pipeline such as data import, visualization and algorithmic processing, and exporting visualizations for information dissemination, commonly performed through intuitive graphical user interfaces [4] . As has been shown in prior work in benchmarking for relational data [8, 9, 17], interactive visualization and analysis workloads can trigger distinct queries and bursty frequencies, which existing graph analytics benchmarks do not simulate [18, 25] . 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problem does the paper address in interactive network visualization and analysis (INVA)?","Question",{"text":75,"@type":76},"It addresses the gap in benchmarking efforts for evaluating how well existing graph systems support interactive network data understanding with human-in-the-loop workflows.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the proposed benchmarking framework evaluate graph systems for INVA?",{"text":80,"@type":76},"It builds a task-centered model of how analysts perform INVA to generate representative INVA workloads, then measures system performance across these workloads on large-scale networks.",{"name":82,"@type":73,"acceptedAnswer":83},"What key findings emerge from the benchmarking results?",{"text":84,"@type":76},"The results show a gap in both supported analysis capabilities and performance for INVA use cases, and they reveal possible correctness issues in some evaluated graph 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