A growth model for citations networks

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The paper studies growth processes in citation networks, contrasting settings where links can change after node creation (e.g., WWW hyperlinks) with citation networks where outgoing references are fixed at publication while incoming links accrue. It proposes a network growth model in which the observed power-law distribution of outgoing links can be shaped by the presence of communities rather than being purely random or purely preferential. The key finding is that community structure can account for how authors’ numbers of references are distributed in a way consistent with preferential-like scaling. A major caveat is that the work is presented primarily as a model description rather than an explicitly detailed empirical test in the provided text, and the study is a preprint/journal transition rather than fully peer-reviewed in the text excerpt. The paper does not explicitly discuss endometriosis or adenomyosis; it was included in the corpus via a keyword match in the upstream search index.

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Abstract

Abstract Complex networks can exhibit different behaviors, there can be networks where the nodes, after being created, can continue to increase or decrease the number of their incoming and/or outgoing links. An example of these networks is the WWW, where the webmaster can add or delete hyperlinks on the web pages under their responsibility at any time. There are also networks where this can not happen. For example, in citation networks of scientific articles, after an article has been published, it will start gaining incoming links as it is cited, but the outgoing links will remain unchanged. Although articles are published with a fixed number of references, the distribution of their outgoing links follows a power law, as if they were the result of a preferential process. So how can we explain that the number of references an author includes in a scientific article is not purely random? In this work, a growth model of this type of complex network is presented, where it is proposed that the distribution of outgoing links can be shaped by the presence of communities.
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A growth model for citations networks | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A growth model for citations networks Pedro Castillo-Castillo, Enrique Stevens-Navarro, Ulises Pineda-Rico, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4993630/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 15 Jan, 2025 Read the published version in Applied Network Science → Version 1 posted 10 You are reading this latest preprint version Abstract Complex networks can exhibit different behaviors, there can be networks where the nodes, after being created, can continue to increase or decrease the number of their incoming and/or outgoing links. An example of these networks is the WWW, where the webmaster can add or delete hyperlinks on the web pages under their responsibility at any time. There are also networks where this can not happen. For example, in citation networks of scientific articles, after an article has been published, it will start gaining incoming links as it is cited, but the outgoing links will remain unchanged. Although articles are published with a fixed number of references, the distribution of their outgoing links follows a power law, as if they were the result of a preferential process. So how can we explain that the number of references an author includes in a scientific article is not purely random? In this work, a growth model of this type of complex network is presented, where it is proposed that the distribution of outgoing links can be shaped by the presence of communities. Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 15 Jan, 2025 Read the published version in Applied Network Science → Version 1 posted Editorial decision: Revision requested 27 Nov, 2024 Reviews received at journal 27 Nov, 2024 Reviewers agreed at journal 19 Nov, 2024 Reviews received at journal 10 Oct, 2024 Reviewers agreed at journal 01 Oct, 2024 Reviewers agreed at journal 30 Sep, 2024 Reviewers invited by journal 24 Sep, 2024 Editor assigned by journal 28 Aug, 2024 Submission checks completed at journal 28 Aug, 2024 First submitted to journal 28 Aug, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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