[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-85325-en":3,"doc-seo-85325-105":30,"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},85325,1099514068365,"Aurelia","https://ap-avatar.wpscdn.com/avatar/10000253d8d9f28188e?_k=1776742907772140068",8,"Research & Report","DiffLens: A Visualization System to Explore Local Differences in Graph Sampling","Graph sampling techniques simplify large-network computation and visualization but inevitably change the sampled graphs, affecting nodes, edges, and structural relationships. Understanding these discrepancies helps users compare sampling trade-offs and enables developers to evaluate their methods, yet no systematic solution exists. This work introduces generic quantitative measures for three difference categories—neighbor-, path-, and structure-based—then presents DiffLens, an interactive visualization system with lens designs to inspect local regions, validated through case studies and a user study.","arXiv :2607 . 11424v1 [ cs .HC] 13 Jul 2026  \nDiffLens: A Visualization System to Explore Local Differences  \nin Graph Sampling  \nZhiguang Zhou†1,Yong Zhang 1 , Yuming Ma 1 , Yuqi Zhou 1 , Ke Lu 1 , Yong Wang‡2 , Yuhua Liu 1 , Jingfang Mao3 , Yongheng Wang4 , Ying Zhao5 , Wei Chen6  \n1 Hangzhou Dianzi University, Hangzhou, China {zhgzhou, 221330016, mamingming, 241330039, luke19999, liuyuhua, }@[hdu.edu.cn](hdu.edu.cn)  \n2 Nanyang Technological University, [Singapore yong-wang@ntu.edu.sg](Singapore yong-wang@ntu.edu.sg)  \n3 Zhejiang Company of China National Tobacco Corporation, Hangzhou, [China mjf402@126.com](China mjf402@126.com)  \n4 Zhejiang Lab, HangZhou, [China wangyh@zhejianglab.org](China wangyh@zhejianglab.org)  \n5 Central South University, Changsha, [China zhaoying@csu.edu.cn](China zhaoying@csu.edu.cn)  \n6 Zhejiang University, Hangzhou, [China chenvis@zju.edu.cn](China chenvis@zju.edu.cn)  \nAbstract  \nGraph sampling techniques have been widely used to simplify network computation and visualization, which also results in inevitable differences between the sampled networks and the original networks in terms of nodes, edges and structures. Investigating such differences can inform graph sampling technique users of the pros and cons of different techniques and select the appropriate one, and can also help graph sampling developers evaluate their own technique. However, there are still no systematic ways to achieve such a goal. This paper fills this research gap by first proposing systematic and generic quantitative measures to quantify three categories of graph differences (i.e., neighbor-based, path-based, and structure-based). Built upon this, we further propose DiffLens, a novel visualization system to help graph sampling developers and users intuitively explore local differences at different regions of their interest within a sampled graph, where three new lens-based visual designs are pre  \nsented to display the neighbor-based, path-based, and structure-based differences respectively. We conducted two case studies and a user study using real-world network datasets to evaluate DiffLens. The results confirmed its effectiveness and usability in helping users explore local differences and compare different graph sampling strategies.  \nCCS Concepts  \n• Human-centered computing → Visual analytics; Information visualization;  \n1 Introduction  \nA variety of graph sampling strategies have been developed to reduce the sizes of large networks, thereby accelerating graph computation and simplifying graph visualization [YK20] . Despite their specific goals and desired properties, it is inevitable to filter out a large number of nodes and edges, thus bringing differences between the sampled and original graphs, such as the changes in node degrees, shortest paths, and community structures. For example, the absence of certain nodes can substantially mislead the estimation of distance, cohesion or other structural measures [SMM17] . Fig. 1 illustrates three local regions of a sampled graph. Node Ais prominent in the original network, but its influence largely declined in the sampled graph due to the removal of its neighbors. Nodes B and C are a pair of nodes with a short distance, but their distance is enlarged in the sampled graph due to the removal of key bridge node D. When node E is filtered out in the sampled graph,  \n† Corresponding author ‡ Corresponding author  \nFigure 1: Local Structural Changes Induced by Graph Sampling  \ntwo connected clusters are broken up making more nodes cannot be linked with each other. Such differences are ubiquitous in sampled graphs, which bring considerable uncertainties and errors to network exploration and analysis [SM13] .  \nHence, when performing graph sampling, it’s important and beneficial to inform users of the differences between the sampled and original graphs [HL13] . In fact, plenty of metrics have been proposed to measure the differences in various attributes between sampled and orig","cbCairFKmRN1stTF","https://ap.wps.com/l/cbCairFKmRN1stTF","pdf",8518304,2,1,15,"English","en",105,"# Introduction\n## Local differences caused by graph sampling\n## Limitations of existing global evaluation metrics\n## Motivation for local, lens-based analysis","[{\"question\":\"Why do graph sampling methods cause unavoidable differences between sampled and original graphs?\",\"answer\":\"Sampling filters out many nodes and edges, which changes node degrees, shortest paths, and community structures in the sampled graph compared with the original graph.\"},{\"question\":\"What categories of graph differences does the paper propose to quantify?\",\"answer\":\"The paper proposes systematic quantitative measures for three categories: neighbor-based, path-based, and structure-based differences.\"},{\"question\":\"How does DiffLens help users explore differences in sampled graphs?\",\"answer\":\"DiffLens provides lens-based visualization designs that allow intuitive exploration of local differences in regions of interest, supporting comparisons across sampling 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do graph sampling methods cause unavoidable differences between sampled and original graphs?","Question",{"text":75,"@type":76},"Sampling filters out many nodes and edges, which changes node degrees, shortest paths, and community structures in the sampled graph compared with the original graph.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What categories of graph differences does the paper propose to quantify?",{"text":80,"@type":76},"The paper proposes systematic quantitative measures for three categories: neighbor-based, path-based, and structure-based differences.",{"name":82,"@type":73,"acceptedAnswer":83},"How does DiffLens help users explore differences in sampled graphs?",{"text":84,"@type":76},"DiffLens provides lens-based visualization designs that allow intuitive exploration of local differences in regions of interest, supporting comparisons across sampling 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