[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-82654-en":3,"doc-seo-82654-105":29,"detail-sidebar-cat-0-en-105":90},{"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":4,"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},82654,1649267921044,"Ava Thompson","https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1782875107921204101",8,"Research & Report","Visual Analytics of Neighborhood Attribute Profiles for Exploring Structural Equivalence","Exploring similar nodes in attributed networks is a core data-mining task. This work introduces a visual analytics approach that uses dimensionality reduction to reveal the topological structure of high-dimensional feature spaces formed by neighborhood attribute profiles. Inter-firm transaction analysis shows structural roles embedded as complex, non-linear manifolds with density biases. Comparison with industry classifications reveals continuous supply-chain transitions, clear separation of semantically identical categories, and fragmentation of a single label. Results indicate limits of assuming semantic equality implies structural similarity, motivating similarity metrics aligned to manifold topology.","Visual Analytics of Neighborhood Attribute Profiles for Exploring  \nStructural Equivalence  \nKohei Arimoto * Teikoku Databank, Ltd.  \nMasahiko Itoh †  \nHokkaido Information University  \narXiv :2607 .02 163v 1 [ cs .HC] 2 Jul 2026  \nFigure 1: Visual verification of the high-dimensional feature space of neighborhood attribute profiles and industry classificationsin an inter-firm transaction network. (A) UMAP projection results for all nodes. The data forms a complex, non-linear manifold accompanied by density biases. (B–H) Overlaid display of seven manufacturing categories (B: Steel, C: Auto Parts, D: Automobiles, E: Trucks, F: Motorcycles, G: Shipbuilding, H: Construction Machinery) . The following three characteristics can be observed from the visualization results: (1) The supply chain hierarchy (B: Steel → C: Auto Parts → D: Automobiles) transitions continuously across the manifold. (2) Transportation equipment (D, E, F, G), which is often treated identically under general industry classifications, is clearly separated into different regions due to differences in actual transaction networks. (3) Construction Machinery (H), which shares a single industry label, is fragmented into multiple regions. This visual evidence challenges the intuitive assumption that identical semantic classifications imply similar structural roles, suggesting that interpreting the manifold’s topology may be crucial for evaluating true similarity.  \nABSTRACT  \nExploring similar nodes in attributed networks represents a key challenge in data mining. While recent representation learning methods embed networks into low-dimensional vectors, they often implicitly assume a uniform and continuous feature space. This paper proposes a visual analytics approach using dimensionality reduction to help clarify the true topological structure of highdimensional feature spaces formed by nodes’ neighborhood attribute profiles. Analyzing inter-firm transaction networks indicates that structural roles can form complex, non-linear manifolds with density biases. Comparing this feature space with industry classifications suggested: (1) supply chain hierarchies transition continuously; (2) categories treated identically under general semantics can be clearly separated by actual transaction networks; and (3) a single industry label may fragment into multiple regions. These findings suggest potential limitations in assuming identical seman-  \n* e-mail: [khyarmt@plusf.jp](khyarmt@plusf.jp)  \n†e-mail: [imash@do-johodai.ac.jp](imash@do-johodai.ac.jp)  \ntics imply similar structural roles and highlight the possible need for new similarity metrics aligned with manifold topology.  \nIndex Terms: Visual Analytics, Attributed Networks, Structural Equivalence, Dimensionality Reduction.  \n1 INTRODUCTION  \nAttributed networks are frequently observed in various application domains and take diverse forms. The data ranges from simple topological information (e.g., direct links in social graphs) to integrations of rich metadata held by each node (e.g., categorical variables like industry type or size in inter-firm transactions) . In many cases, these data are composed of combinations of complex connectionsand diverse attribute variables.  \nBased on such attributed networks, users often need to explore data and attempt to answer complex structural questions. For instance, consider an analyst searching an inter-firm transaction network for potential customers or competitors that share characteristics similar to a specific company. To extract true value from this type of data, it is often necessary to (1) capture the roles within the network beyond mere direct connections (proximity), (2) analyze the attribute composition of neighboring nodes (e.g., the proportion of transactions with specific industry groups), and (3) distinguish  \nand compare the general semantic similarity of business activities against true structural equivalence based on network topology.  \nAgainst the backdrop of the impo","cbCaitRsu5RByUIE","https://ap.wps.com/l/cbCaitRsu5RByUIE","pdf",1558680,1,5,"English","en",105,"# Introduction\n## Attributed networks and structural similarity challenges\n## Role representation methods and limitations\n## Visual analytics approach and contributions","[{\"question\":\"What problem does the document address in attributed networks?\",\"answer\":\"It addresses how to explore similar nodes by uncovering structural equivalence beyond direct links and simple semantic attribute similarity.\"},{\"question\":\"How does the proposed approach examine neighborhood attribute profiles?\",\"answer\":\"It applies dimensionality reduction to visualize and analyze the true topological structure of high-dimensional feature spaces derived from nodes’ neighborhood attribute profiles.\"},{\"question\":\"What three patterns are found when comparing the feature space to industry classifications?\",\"answer\":\"The visualization shows (1) supply-chain hierarchies transition continuously, (2) categories treated identically under general semantics can separate into different regions, and (3) a single industry label may fragment into multiple 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