[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127139-en":3,"doc-seo-127139-105":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":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":27,"seo_description":14,"update_tm":28,"read_time":29},127139,687207022233,"Riley","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Leveraging Advances in Machine Learning for Robust Network Classification and Interpretation","The document addresses the need to simulate and classify realistic networks using empirical data across scientific fields such as epidemiology and computer science. It highlights a gap in existing tools: many network generative models (e.g., Erdös-Rényi, small-world) lack practical ways to assess whether a chosen model captures the structure of a given network. The work applies interpretable machine learning to classify simulated networks from generative-model outputs using primary network attributes and their interactions. Results emphasize how specific features and feature interactions distinguish models, explain complex network structures, and clarify how real-world networks form.","arXiv :2403 . 132 15v2 [ cs . SI] 12 Jun 2024  \nLeveraging advances in machine learning for the robust classification and interpretation of  \nnetworks.  \nRaima Carol Appaw 1∗, Nicholas Fountain-Jones 1 , Michael A. Charleston 1 1 School of Natural Sciences, University of Tasmania, Hobart Australia 7001 .  \nJune 13, 2024  \nAbstract  \nThe ability to simulate realistic networks based on empirical data is  \nan important task across scientific disciplines, from epidemiology to com  \nputer science. Often simulation approaches involve selecting a suitable  \nnetwork generative model such as Erdös-Rényi or small-world. However,  \nfew tools are available to quantify if a particular generative model is suit  \nable for capturing a given network structure or organization. We utilize  \nadvances in interpretable machine learning to classify simulated networks  \nby our generative models based on various network attributes, using both  \nprimary features and their interactions. Our study underscores the signif  \nicance of specific network features and their interactions in distinguishing  \ngenerative models, comprehending complex network structures, and the  \nformation of real-world networks.  \n1 Introduction  \nReal-world network data derived from physical systems such as ecological food webs, biochemical pathways, genetic interactions, animal social behavior, and biological processes, captures complex relationships and addresses fundamental questions about species adaptability, ecosystem dynamics, pathogen dynamics, social dynamics, and genetic regulatory networks [4, 11, 19, 20, 31, 37] . The multi-dimensional nature and dynamic interactions among variables over time in these systems pose a challenge to their classification. Traditional classification methods (such as decision trees, support vector machines, k-nearest neighbor, and logistic regression) struggle to capture these complexities effectively [2, 29, 55, 60] . Moreover, the lack of interpretability in machine learning models further compounds this challenge.  \nMachine learning techniques, including supervised learning, have been applied to the classification of real-world networks into theoretical network models,  \nalso known as generative models. Jansen et al. [28] introduced the Alternating Decision Tree (ADT), an unsupervised algorithm designed for network classification by focusing on model selection through synthetic network fitting to real network data. Their approach involved generating 1000 graphs and extracting various network features, training the ADT using nine classifiers for effective model selection. Similarly, Ikehara et al. [27] conducted a study on 986 realnetworks and 575 generated networks, utilizing binary classification with graph features. Barnett et al. [9] employed a random forest algorithm for network classification, manually selecting individual graph features, while Canning et al. [13] proposed a random forest approach for classifying 529 networks. Although these efforts to address network similarity, feature identification, and optimization, the studies did not consider the impact of feature interaction on classification. Graph similarity metrics, such as degree distribution and clustering, have been utilized to compare graph centrality measures between real-world and theoretical networks [2, 23, 29, 45, 51] . Although some measures show effectiveness in classifying real-world networks, there remains potential for enhancing the efficiency of these methods.  \nVarious network models such as Erdös-Rényi, scale-free, small-world, stochasticblock model, and spatial networks, have been used to analyze real-world data [7, 17, 31, 47, 56, 62, 64] . These network models are straightforward to simulate and facilitate the testing of new and diverse network-related hypotheses. Using these network models characterized by different network structures and dynamics has set the stage for pioneering research, offering the potential to classify empirical networks effective","cbCaigWIyl8o01tq","https://ap.wps.com/l/cbCaigWIyl8o01tq","pdf",28111986,1,48,"English","en",105,"# Abstract\n# Introduction\n## Challenges in classifying real-world networks\n## Prior machine-learning approaches and limitations\n## Common network generative models and open questions\n## Study aims and interpretability goals","[{\"question\":\"Why is selecting a suitable network generative model difficult?\",\"answer\":\"Many simulation approaches use established generative models, but few tools exist to quantify whether a particular model can capture the structure of an observed network.\"},{\"question\":\"How does the study use interpretable machine learning for network classification?\",\"answer\":\"It classifies simulated networks by generative models using network attributes, including both primary features and interactions between features.\"},{\"question\":\"What is the main insight about network features?\",\"answer\":\"The study underscores the significance of specific network features and their interactions in distinguishing generative models, understanding complex structures, and explaining real-world network formation.\"}]","Leveraging Advances in Machine Learning for Robust Network Classification and Interpretation | PDF",1785937124,121,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"leveraging-advances-in-machine-learning-for-robust-network-classification-and-interpretation","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/leveraging-advances-in-machine-learning-for-robust-network-classification-and-interpretation/127139/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is selecting a suitable network generative model difficult?","Question",{"text":75,"@type":76},"Many simulation approaches use established generative models, but few tools exist to quantify whether a particular model can capture the structure of an observed network.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the study use interpretable machine learning for network classification?",{"text":80,"@type":76},"It classifies simulated networks by generative models using network attributes, including both primary features and interactions between features.",{"name":82,"@type":73,"acceptedAnswer":83},"What is the main insight about network features?",{"text":84,"@type":76},"The study underscores the significance of specific network features and their interactions in distinguishing generative models, understanding complex structures, and explaining real-world network formation.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]