[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-265143-105":59,"doc-detail-265143-en":131},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":124,"head_meta":126,"extra_data":128,"updated_unix":130},105,"en","an-efficient-probabilistic-approach-for-graph-similarity-search-extended-technical-report","An Efficient Probabilistic Approach for Graph Similarity Search - Extended Technical Report","","Graph similarity search is a fundamental operation in graph databases, where Graph Edit Distance (GED) is widely used for its interpretability and applicability. Exact GED computation is NP-hard and can be inefficient on large graphs, yet many applications need fast, approximate results rather than exact accuracy. This paper proposes a probabilistic method estimating GED through a branch-based similarity measure, Graph Branch Distance (GBD). Experiments on real and synthetic datasets demonstrate improved accuracy, efficiency, and scalability over comparable approaches.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/an-efficient-probabilistic-approach-for-graph-similarity-search-extended-technical-report/265143/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/an-efficient-probabilistic-approach-for-graph-similarity-search-extended-technical-report/265143.png","ImageObject",300,407,{"name":92,"@type":93},"Sage","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-04","2026-09-14",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",11,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"Why is exact Graph Edit Distance (GED) computation inefficient for large graphs?","Question",{"text":113,"@type":114},"Exact GED computation is NP-hard, which can lead to unsatisfactory computational efficiency when performing similarity search over large graphs.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"What is Graph Branch Distance (GBD) in this approach?",{"text":118,"@type":114},"GBD is a branch-comparison-based similarity measure that uses branches as elementary graph structures and can be computed in polynomial time.",{"name":120,"@type":111,"acceptedAnswer":121},"How does the method estimate GED using GBD?",{"text":122,"@type":114},"The approach models variations between branches as arising from graph edit operations, establishing a probabilistic relationship so that GED between two graphs can be efficiently estimated from their GBD.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},265143,1789392748,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":130,"read_time":36,"preview_page_count":4},687197207057,"https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0","An Efficient Probabilistic Approach for Graph Similarity Search [Extended Technical Report]  \nZijian Li \\#1 , Xun Jian \\#2 , Xiang Lian 􀀃3, Lei Chen \\#4  \n\\# Computer Science and Engineering Department, HKUST, Hong Kong, China  \n[1](1 zlicb@cse.ust.hk 2 xjian@cse.ust.hk 4 leichen@cse.ust.hk)[ zlicb@cse.ust.hk](1 zlicb@cse.ust.hk 2 xjian@cse.ust.hk 4 leichen@cse.ust.hk)[ 2](1 zlicb@cse.ust.hk 2 xjian@cse.ust.hk 4 leichen@cse.ust.hk)[ xjian@cse.ust.hk](1 zlicb@cse.ust.hk 2 xjian@cse.ust.hk 4 leichen@cse.ust.hk)[ 4](1 zlicb@cse.ust.hk 2 xjian@cse.ust.hk 4 leichen@cse.ust.hk)[ leichen@cse.ust.hk](1 zlicb@cse.ust.hk 2 xjian@cse.ust.hk 4 leichen@cse.ust.hk)  \n􀀃 Computer Science Department, Kent State University, Kent, USA  \n[3](3 xlian@kent.edu)[ xlian@kent.edu](3 xlian@kent.edu)  \narXiv : 1706 .05476v2 [ cs .DB] 23 Jan 2018  \nAbstract—Graph similarity search is a common and fundamental operation in graph databases. One of the most popular graph similarity measures is the Graph Edit Distance (GED) mainly because of its broad applicability and high interpretability. Despite its prevalence, exact GED computation is proved to be NP-hard, which could result in unsatisfactory computational efficiencyon large graphs. However, exactly accurate search results are usually unnecessary for real-world applications especially when the responsiveness is far more important than the accuracy. Thus, in this paper, we propose a novel probabilistic approach to efficiently estimate GED, which is further leveraged for the graph similarity search. Specifically, we first take branches as elementary structures in graphs, and introduce a novel graph similarity measure by comparing branches between graphs, i.e., Graph Branch Distance (GBD), which can be efficiently calculated in polynomial time. Then, we formulate the relationship between GED and GBD by considering branch variations asthe result ascribed to graph edit operations, and model this process by probabilistic approaches. By applying our model, the GED between any two graphs can be efficiently estimated by their GBD, and these estimations are finally utilized in the graph similarity search. Extensive experiments show that our approach has better accuracy, efficiency and scalability than other comparable methods in the graph similarity search over real and synthetic data sets.  \nI. INTRODUCTION  \nGraph similarity search is a common and fundamental operation in graph databases, which has widespread applications in various fields including bio-informatics, sociology, and chemical analysis, over the past few decades. For evaluating the similarity between graphs, Graph Edit Distance (GED)  \n[1] is one of the most prevalent measures because of its wide applicability, that is, GED is capable of dealing with various kinds of graphs including directed and undirected graphs, labeled and unlabeled graphs, as well as simple graphsand multi-graphs (which could have multiple edges between two vertices) . Furthermore, GED has high interpretability, since it corresponds to some sequences of concrete graph edit operations (including insertion of vertices and edges, etc.) of minimal lengths, rather than implicit graph embedding utilized in spectral [2] or kernel [3] measures. Example 1 illustrates the basic idea of GED.  \nExample 1: Assume that we have two graphs G 1 and G2 as shown in Figure 1 . The label sets of graph G 1 's vertices and edges are fA; B; Cg and fy; y; zg, respectively, and the label sets of graph G2 's vertices and edges are fA; B; Cg  \nand fx; y; zg, respectively. The Graph Edit Distance (GED) between G 1 and G2 is the minimal number of graph edit operations to transform G 1 into G2. It can be proved that the GED between G 1 and G2 is 3, which can be achieved by 􀀍1 deleting the edge between v 1 and v3 in G 1 , and  inserting an isolated vertex v4 with label A, and  inserting an edge between v3 and v4 with label x.  \nWith GED as the graph similarity measure, the graph similarity search problem is formally st","cbCaikewEyhGxlVE","https://ap.wps.com/l/cbCaikewEyhGxlVE","pdf",1077552,16,"English","# Introduction\n## Graph Similarity Search and GED\n## Problem Statement\n## Challenges of Exact GED Computation\n## Motivation for Probabilistic Approximation\n## Overview of Proposed Approach","[{\"question\":\"Why is exact Graph Edit Distance (GED) computation inefficient for large graphs?\",\"answer\":\"Exact GED computation is NP-hard, which can lead to unsatisfactory computational efficiency when performing similarity search over large graphs.\"},{\"question\":\"What is Graph Branch Distance (GBD) in this approach?\",\"answer\":\"GBD is a branch-comparison-based similarity measure that uses branches as elementary graph structures and can be computed in polynomial time.\"},{\"question\":\"How does the method estimate GED using GBD?\",\"answer\":\"The approach models variations between branches as arising from graph edit operations, establishing a probabilistic relationship so that GED between two graphs can be efficiently estimated from their GBD.\"}]","An Efficient Probabilistic Approach for Graph Similarity Search - Extended Technical Report | PDF"]