[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120835-en":3,"doc-seo-120835-105":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},120835,16904993612988,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Machine learning-based prediction of Q-voter model in complex networks","This article applies machine learning to predict two key dynamical quantities of the Q-voter model on complex networks: consensus time and the frequency of opinion changes. Using nine network topological measures, it identifies clustering coefficient and information centrality as the most influential predictors. The proposed models maintain high accuracy under three distinct Q-voter initialization schemes, including random choices and biased selection of high- and low-degree agents with positive opinions. The study connects network structure to polarization-related dynamics in social systems.","arXiv :2310 .09131v1 [physics .soc-ph] 13 Oct 2023  \nMachine learning-based prediction of Q-voter model in complex networks  \nAruane M. Pineda  \nInstitute of Mathematical and Computer Sciences, University of S˜ao Paulo, S˜ao Carlos, S˜ao Paulo, Brazil  \nMathematics Institute, University of Warwick, Coventry, England, UK  \nE-mail: [aruane.pineda@usp.br](aruane.pineda@usp.br)  \nPaul Kent  \nMathematics Institute, University of Warwick, Coventry, England, UK  \nE-mail: [Paul.Kent@warwick.ac.uk](Paul.Kent@warwick.ac.uk)  \nColm Connaughton  \nMathematics Institute, University of Warwick, Coventry, England, UK  \nLondon Mathematical Laboratory, London, England, UK  \nE-mail: [c.connaughton@lml.org.uk](c.connaughton@lml.org.uk)  \nFrancisco A. Rodrigues  \nInstitute of Mathematical and Computer Sciences, University of S˜ao Paulo, S˜ao Carlos, S˜ao Paulo, Brazil  \nE-mail: [francisco@icmc.usp.br](francisco@icmc.usp.br)  \nAbstract.  \nIn this article, we consider machine learning algorithms to accurately predict two variables associated with the Q-voter model in complex networks, i.e., (i) the consensus time and (ii) the frequency of opinion changes. Leveraging nine topological measures of the underlying networks, we verify that the clustering coefficient (C) and information centrality (IC) emerge as the most important predictors for these outcomes. Notably, the machine learning algorithms demonstrate accuracy across three distinct initialization methods of the Q-voter model, including random selection and the involvement of high-and low-degree agents with positive opinions. By unraveling the intricate interplay between network structure and dynamics, this research shedslight on the underlying mechanisms responsible for polarization effects and other dynamic patterns in social systems. Adopting a holistic approach that comprehends the complexity of network systems, this study offers insights into the intricate dynamics associated with polarization effects and paves the way for investigating the structure and dynamics of complex systems through modern methods of machine learning.  \nMachine learning-based prediction of Q-voter model in complex networks 2  \nKeywords: Complex networks structure, Q-voter model, Polarization, Network measures, Machine learning algorithms  \n1. Introduction  \nInteractions among the components of a complex system have given rise to properties not present in its isolated parts [1] . For instance, the collective behavior of ants in a colony provides a compelling illustration of emergence. While individually following simple rules, ants exhibit complex behaviors such as efficient food foraging, elaborate nest construction, and coordinated defense [2] . Such an emergence phenomenon significantly extends beyond the natural world, since it also manifests within our society through intricate interactions among agents, groups, and institutions.  \nA substantial consequence of emergence is social polarization, according to which agents develop increasingly extreme opinions and display diminished tolerance for opposing viewpoints, ultimately leading to societal divisions. Numerous studies have associated the phenomenon with negative outcomes in political contexts, as seen in the recent elections in both Brazil and the United States [3, 4, 5, 6] . In Brazil, heightened polarization culminated in a significant event on January 8, 2023, when key institutions in Bras´ılia, the capital of Brazil, were invaded. This event was the result of escalating tensions stemming from polarized political discourse. The Supreme Federal Court, the National Congress building, and the Presidential Palace were among the targeted institutions. Similarly, the United States also faced its own challenges associated with polarization. A notable incident occurred on January 6, 2021, when a crowd stormed the United States Capitol in an attempt to overturn the results of the presidential election. Therefore, the causes and effects of polarization in social netw","cbCaifrJbJAJUc2W","https://ap.wps.com/l/cbCaifrJbJAJUc2W","pdf",2226276,1,32,"English","en",105,"# Introduction\n## Emergence and social polarization\n## Mathematical models for opinion dynamics\n## Network topology and consensus formation","[{\"question\":\"Which outcomes does the study predict for the Q-voter model on complex networks?\",\"answer\":\"It predicts consensus time and the frequency of opinion changes associated with the Q-voter dynamics.\"},{\"question\":\"Which network measures are found to be the most important predictors?\",\"answer\":\"Clustering coefficient and information centrality emerge as the most important predictors for the predicted outcomes.\"},{\"question\":\"Does prediction accuracy depend on how the Q-voter model is initialized?\",\"answer\":\"The machine learning algorithms demonstrate accuracy across three different initialization methods, including random selection and biased involvement of high- and low-degree agents with positive opinions.\"}]","Machine learning-based prediction of Q-voter model in complex networks | PDF",1785732271,81,{"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},"machine-learning-based-prediction-of-q-voter-model-in-complex-networks","",{"@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/machine-learning-based-prediction-of-q-voter-model-in-complex-networks/120835/",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","2026-08-03",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},"Which outcomes does the study predict for the Q-voter model on complex networks?","Question",{"text":75,"@type":76},"It predicts consensus time and the frequency of opinion changes associated with the Q-voter dynamics.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which network measures are found to be the most important predictors?",{"text":80,"@type":76},"Clustering coefficient and information centrality emerge as the most important predictors for the predicted outcomes.",{"name":82,"@type":73,"acceptedAnswer":83},"Does prediction accuracy depend on how the Q-voter model is initialized?",{"text":84,"@type":76},"The machine learning algorithms demonstrate accuracy across three different initialization methods, including random selection and biased involvement of high- and low-degree agents with positive opinions.","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"]