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        <title>Journal of Social Structure Feed</title>
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            <title>Journal of Social Structure Feed</title>
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            <link>https://sciendo.com/journal/JOSS</link>
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        <copyright>All rights reserved 2026, International Network for Social Network Analysis (INSNA)</copyright>
        <item>
            <title><![CDATA[The Role of Self-Monitoring in Shaping Friendship and Support Networks: Evidence from a University Student Volunteer Association]]></title>
            <link>https://sciendo.com/article/10.21307/joss-2026-001</link>
            <guid>https://sciendo.com/article/10.21307/joss-2026-001</guid>
            <pubDate>Wed, 25 Mar 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[

Understanding how students form and maintain social networks in volunteer organizations is crucial for promoting collaboration, effective communication, and the flow of resources within these groups. While prior research has often relied on self-reported networks, few studies have examined the discrepancies between perceived and actual social ties, particularly within student volunteer associations in Romania. The present study analyzed personal friendship and help networks in the human resources department of a university-based student volunteer association. Central to the investigation was self-monitoring, a variable introduced by Snyder (1974), examined in relation to cognitive social networks, network centrality, physical proximity, and dependency relationships. Statistical and network analyses revealed that students with higher self-monitoring scores demonstrated greater precision in identifying relationships, occupied more central positions within both friendship and help networks, maintained more connections, and strategically managed dependency by offering help more often than requesting it. Additionally, students residing on the association’s campus were considered close friends and received more help requests than those living elsewhere, highlighting the role of physical proximity in shaping relational patterns and network popularity. These findings provide insights into how individual traits and contextual factors jointly shape social network formation in student volunteer organizations and extend understanding of cognitive and structural dynamics in these settings.
]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[The Well-Connected Animal: Social Networks and the Wondrous Complexity of Animal Societies]]></title>
            <link>https://sciendo.com/article/10.21307/joss-2026-002</link>
            <guid>https://sciendo.com/article/10.21307/joss-2026-002</guid>
            <pubDate>Fri, 20 Feb 2026 00:00:00 GMT</pubDate>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[Book Reviews]]></title>
            <link>https://sciendo.com/article/10.21307/joss-2025-002</link>
            <guid>https://sciendo.com/article/10.21307/joss-2025-002</guid>
            <pubDate>Sat, 01 Nov 2025 00:00:00 GMT</pubDate>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[Conceptualizing and Modeling Relational Processes: Introducing Disjointed Fluidity]]></title>
            <link>https://sciendo.com/article/10.21307/joss-2025-001</link>
            <guid>https://sciendo.com/article/10.21307/joss-2025-001</guid>
            <pubDate>Tue, 01 Apr 2025 00:00:00 GMT</pubDate>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[Network Analysis: Integrating Social Network Theory, Method, and Application with R]]></title>
            <link>https://sciendo.com/article/10-21307/joss-2024-002</link>
            <guid>https://sciendo.com/article/10-21307/joss-2024-002</guid>
            <pubDate>Thu, 05 Sep 2024 00:00:00 GMT</pubDate>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[An Analysis of Correlation and Comparisons Between Centrality Measures in Network Models]]></title>
            <link>https://sciendo.com/article/10-21307/joss-2024-001</link>
            <guid>https://sciendo.com/article/10-21307/joss-2024-001</guid>
            <pubDate>Sat, 20 Jan 2024 00:00:00 GMT</pubDate>
            <description><![CDATA[

Centrality measures are widely utilized in complex networks to assess the importance of nodes. The choice of measure depends on the network type, leading to diverse node rankings. This paper aims to compare various centrality measures by examining their correlations. We specifically focus on the Pearson correlation coefficient and Spearman correlation. Pearson correlation considers node centrality values, while Spearman correlation is based on node ranks. Our study encompasses different network topologies, including random, scale-free, and small-world networks. We investigate how these network structures influence correlation values. The main part of the paper describes the relationship between correlations and network model parameters. Additionally, we explore the impact of global network characteristics on correlations, as well as their direct connection to network parameters. Through a systematic review of literature-based centrality measures, we have identified and selected the most commonly employed ones to investigate their correlation including degree centrality, betweenness centrality, eigenvector centrality, and closeness centrality. Our findings reveal that correlations in random networks are minimally affected by network structure, whereas restructuring significantly impacts correlations in other networks. In particular, we show a notable impact of structural parameter variations on correlations within small-world networks. Furthermore, we demonstrate the substantial influence of fundamental network characteristics such as spectral gap, global efficiency, and majorization gap on correlations. We show that amongst the various properties, the spectral gap stands out as the most valuable indicator for estimating correlations.
]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[Longitudinal Network Models]]></title>
            <link>https://sciendo.com/article/10.21307/joss-2023-005</link>
            <guid>https://sciendo.com/article/10.21307/joss-2023-005</guid>
            <pubDate>Mon, 20 Nov 2023 00:00:00 GMT</pubDate>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[A Research Agenda for Social Networks and Social Resilience]]></title>
            <link>https://sciendo.com/article/10.21307/joss-2023-004</link>
            <guid>https://sciendo.com/article/10.21307/joss-2023-004</guid>
            <pubDate>Tue, 31 Oct 2023 00:00:00 GMT</pubDate>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[Personal Networks: Classic Readings and New Directions in Egocentric Analysis]]></title>
            <link>https://sciendo.com/article/10.21307/joss-2023-003</link>
            <guid>https://sciendo.com/article/10.21307/joss-2023-003</guid>
            <pubDate>Thu, 26 Oct 2023 00:00:00 GMT</pubDate>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[Trade and Nation: How Companies and Politics Reshaped Economic Thought]]></title>
            <link>https://sciendo.com/article/10.21307/joss-2023-002</link>
            <guid>https://sciendo.com/article/10.21307/joss-2023-002</guid>
            <pubDate>Wed, 13 Sep 2023 00:00:00 GMT</pubDate>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[Living in Networks: The Dynamics of Social Relations]]></title>
            <link>https://sciendo.com/article/10.21307/joss-2023-001</link>
            <guid>https://sciendo.com/article/10.21307/joss-2023-001</guid>
            <pubDate>Tue, 02 May 2023 00:00:00 GMT</pubDate>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[Social Networks of Meaning and Communication]]></title>
            <link>https://sciendo.com/article/10.21307/joss-2022-006</link>
            <guid>https://sciendo.com/article/10.21307/joss-2022-006</guid>
            <pubDate>Sun, 09 Oct 2022 00:00:00 GMT</pubDate>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[Advances in Network Clustering and Blockmodeling]]></title>
            <link>https://sciendo.com/article/10.21307/joss-2022-005</link>
            <guid>https://sciendo.com/article/10.21307/joss-2022-005</guid>
            <pubDate>Wed, 31 Aug 2022 00:00:00 GMT</pubDate>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[An Analysis of Relations Among European Countries Based on UEFA European Football Championship]]></title>
            <link>https://sciendo.com/article/10.21307/joss-2022-004</link>
            <guid>https://sciendo.com/article/10.21307/joss-2022-004</guid>
            <pubDate>Sun, 14 Aug 2022 00:00:00 GMT</pubDate>
            <description><![CDATA[

With the increasing globalization in the 21st century, football has become more of an industry than a sport that supports tremendous amount of money circulation. More players started to play in countries different from their original nationality. Some countries used this evolution process of football to improve the quality of their leagues. The clubs in these leagues recruited the best players from all around the world. In international football, nations are represented by their best players, and these players might come from a variety of different leagues. To observe the countries that host the best players of these nations, we analyze the trend for the nations represented in the European Football Championship. We construct social networks for the last eight tournaments from 1992 to 2020 and calculate network-level metrics for each. We find the most influential countries for each tournament and analyze the relationship between country influence and economic revenue of football in those countries. We use several clustering algorithms to pinpoint the communities in obtained social networks and discuss the relevance of our findings to cultural and historical events.
]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[An Analysis of Relations Among European Countries Based on UEFA European Football Championship]]></title>
            <link>https://sciendo.com/article/10-21307/joss-2022-004</link>
            <guid>https://sciendo.com/article/10-21307/joss-2022-004</guid>
            <pubDate>Sun, 14 Aug 2022 00:00:00 GMT</pubDate>
            <description><![CDATA[

With the increasing globalization in the 21st century, football has become more of an industry than a sport that supports tremendous amount of money circulation. More players started to play in countries different from their original nationality. Some countries used this evolution process of football to improve the quality of their leagues. The clubs in these leagues recruited the best players from all around the world. In international football, nations are represented by their best players, and these players might come from a variety of different leagues. To observe the countries that host the best players of these nations, we analyze the trend for the nations represented in the European Football Championship. We construct social networks for the last eight tournaments from 1992 to 2020 and calculate network-level metrics for each. We find the most influential countries for each tournament and analyze the relationship between country influence and economic revenue of football in those countries. We use several clustering algorithms to pinpoint the communities in obtained social networks and discuss the relevance of our findings to cultural and historical events.
]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[Inferential Network Analysis]]></title>
            <link>https://sciendo.com/article/10.21307/joss-2022-003</link>
            <guid>https://sciendo.com/article/10.21307/joss-2022-003</guid>
            <pubDate>Thu, 02 Jun 2022 00:00:00 GMT</pubDate>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[A Network Analysis of Twitter's Crackdown on the QAnon Conversation]]></title>
            <link>https://sciendo.com/article/10.21307/joss-2022-002</link>
            <guid>https://sciendo.com/article/10.21307/joss-2022-002</guid>
            <pubDate>Mon, 16 May 2022 00:00:00 GMT</pubDate>
            <description><![CDATA[

The QAnon conspiracy theory holds that former President Trump is fighting a ‘deep-state’ cabal of Satan-worshipping, cannibalistic pedophiles running a global child sex-trafficking ring. Conspirators include liberal Hollywood actors, Democratic politicians, financial elites, and even some religious leaders. Prominent politicians have embraced it, and the media increasingly covered it in the lead-up to the 2020 Presidential Election and beyond. Beginning on 4chan message boards in October 2017, QAnon narratives proliferated across popular social media platforms as individuals engaged in QAnon-related conversations on one platform shared links to ‘reputable’ content on others. In this paper, we draw on insights drawn from studies of diffusion and use social network analysis to analyze the networks generated by Twitter users from sharing external QAnon-related social media content via URLs during two key time frames: (1) the peak of QAnon Twitter activity in the Spring of 2020 and (2) the period following Twitter's crackdown on QAnon activities in July 2020. Our analysis reveals that the tweets and retweets of just a few actors accounted for most of the sharing of links to external social media sites, suggesting that other users saw them as reliable sources of information. It also shows that Twitter's crackdown impacted some aspects of the URL-sharing network. We conclude by briefly considering strategies for countering conspiracy theories and offering suggestions for future research.
]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[Syndicate Women: Gender and Networks in Chicago Organized Crime]]></title>
            <link>https://sciendo.com/article/10.21307/joss-2022-001</link>
            <guid>https://sciendo.com/article/10.21307/joss-2022-001</guid>
            <pubDate>Thu, 21 Apr 2022 00:00:00 GMT</pubDate>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[Book Review: The Oxford Handbook of Social Networks]]></title>
            <link>https://sciendo.com/article/10.21307/joss-2021-002</link>
            <guid>https://sciendo.com/article/10.21307/joss-2021-002</guid>
            <pubDate>Wed, 24 Nov 2021 00:00:00 GMT</pubDate>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[Book Review: Multimodal Political Networks]]></title>
            <link>https://sciendo.com/article/10.21307/joss-2021-001</link>
            <guid>https://sciendo.com/article/10.21307/joss-2021-001</guid>
            <pubDate>Fri, 17 Sep 2021 00:00:00 GMT</pubDate>
            <category>ARTICLE</category>
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