[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-292410-105":59,"doc-detail-292410-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","network-structures-in-proximity-data-introduction","NETWORK STRUCTURES IN PROXIMITY DATA - Introduction","","Proximity data are widely used in social and behavioral sciences to quantify similarity, relatedness, association, and psychological distance between entities. The chapter surveys how proximity measures are obtained from judgments and from co-occurrence, sequential dependency, correlation, and distance, and explains the need to characterize the underlying structure in proximity sets. It contrasts spatial low-dimensional modeling such as multidimensional scaling with discrete network and graph-theoretic representations, then introduces network representations and Pathfinder networks and reviews essential graph theory concepts.",{"@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/network-structures-in-proximity-data-introduction/292410/",{"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/network-structures-in-proximity-data-introduction/292410.png","ImageObject",300,407,{"name":92,"@type":93},"Marry","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-03","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",15,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What is proximity data used for in this chapter?","Question",{"text":113,"@type":114},"Proximity data are used to study similarity, relatedness, association, and psychological distance between entities in human cognition and social processes.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"How does the chapter contrast spatial and discrete modeling approaches?",{"text":118,"@type":114},"Spatial models represent entities as points where relations are captured by distances in a low-dimensional space, while discrete models represent entities as nodes connected by links, often drawing on graph theory.",{"name":120,"@type":111,"acceptedAnswer":121},"What is the role of Pathfinder networks?",{"text":122,"@type":114},"Pathfinder networks are defined as a class of networks derived from proximity data, linking to fundamental graph theory concepts and providing examples of applications across data types.","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},292410,1789651030,{"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":145,"preview_page_count":4},5909892079670,"https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c","NETWORK STRUCTURES IN PROXIMITY DATA  \nRoger W. Schvaneveldt  \nFrancis T. Durso  \nDonald W. Dearholt  \nI. Introduction  \nProximity data are commonplace in the social and behavioral sciences. Judgments of similarity, relatedness, or association between entities are frequently used in the study of human cognition. 1 Investigations of social processes often make use of proximity measures such as liking between pairs of individuals and frequency of communication between individuals or groups of individuals. Proximities can also be obtained from measures of co-occurrence, sequential dependency, correlation, and distance.  \nThis ubiquity of proximity data has encouraged the development of many methods for characterizing the underlying structure in sets of proximities. Some methods, such as multidimensional scaling (Shepard, 1962a, 1962b; Kruskal, 1964, 1977), assume a continuous, low-dimensional space as the underlying model. Spatial models generally represent  \n1 Similarity, relatedness, and psychological distance are closely related concepts indicating the degree to which things belong together psychologically. Proximity is a general term that represents these concepts as well as other measurements, both subjective and objective, of the relationship between pairs of entities. In this chapter, we use the term proximity to refer to such measurements. In the techniques we propose, the measurements have the direction of distances (or distance estimates) so that small values represent similarity, relatedness, or nearness, and large values represent dissimilarity, lack of relatedness, or distance.  \nTHE PSYCHOLOGY OF LEARNING AND MOTIVATION. VOL. 24  \n249 Copyright © 1989 by Academic Press, Inc. All rights of reproduction in any form reserved.  \nentities as points in space and relations between entities are captured in distances between entities in that space. The dimensions of the space often reflect important dimensions of variation in the proximity data. Other methods derive from discrete models that yield hierarchical clusters (Johnson, 1967), overlapping dusters (Shepard & Arabie, 1979), tree structures (Butler & Corter, I986; Cunningham, 1978; Sattath & Tversky, 1977), or networks (Hutchinson, 1981; Feger & Bien, 1982; Schvaneveldt & Durso, 1981; Schvaneveldt, Dearholt, & Durso, 1988) . Discrete models generally represent entities as nodes in networks and relations between entities as links connecting nodes. The patterns of connections among nodes in networks often reflect clustering and other structures in the proximity data. Whereas spatial models have mathematical foundationsin geometry, discrete models often derive from graph theory.  \nThe foundations of multidimensional scaling (MDS) have been explored in some depth (Beals, Krantz, & Tversky, 1968), leading to formal specifications of the assumptions underlying MDS as a model of the psychological representation of stimuli. Considerable work has gone into the development of discrete models, and the connections between discrete models and graph theory are becoming more apparent (Shepard & Arabie, 1979; Schvaneveldt et al. , 1988) . As representations of mental structure, discrete models offer alternatives to spatial models that are often more closely identified with psychological theory, particularly network based models.  \nIn this chapter, we discuss network representations and their relationship to proximity data. Pathfinder, a definition of a class of networks derived from proximity data, is tied to some fundamental concepts in graph theory, and illustrations of the application of Pathfinder networks to a variety of data are presented. Since much of the discussion revolves around graphs and networks, we briefly review some basic concepts.  \nA. GRAPH THEORY  \nGraph theory is the mathematical study of structures cons1stmg of nodes with links connecting some pairs of nodes (Carre, 1979; Christof ides, 1975; Harary, 1969) . Terminology in graph theory varies somewhat from one so","cbCailbDkm0ZRtyV","https://ap.wps.com/l/cbCailbDkm0ZRtyV","pdf",2249849,36,"English","# I. Introduction\n## A. Graph Theory","[{\"question\":\"What is proximity data used for in this chapter?\",\"answer\":\"Proximity data are used to study similarity, relatedness, association, and psychological distance between entities in human cognition and social processes.\"},{\"question\":\"How does the chapter contrast spatial and discrete modeling approaches?\",\"answer\":\"Spatial models represent entities as points where relations are captured by distances in a low-dimensional space, while discrete models represent entities as nodes connected by links, often drawing on graph theory.\"},{\"question\":\"What is the role of Pathfinder networks?\",\"answer\":\"Pathfinder networks are defined as a class of networks derived from proximity data, linking to fundamental graph theory concepts and providing examples of applications across data types.\"}]","NETWORK STRUCTURES IN PROXIMITY DATA - Introduction | PDF",91]