rss_2.0Journal of Social Structure FeedSciendo RSS Feed for Journal of Social Structurehttps://sciendo.com/journal/JOSShttps://www.sciendo.comJournal of Social Structure Feedhttps://sciendo-parsed.s3.eu-central-1.amazonaws.com/64722bdf215d2f6c89dbe8e4/cover-image.jpghttps://sciendo.com/journal/JOSS140216An Analysis of Correlation and Comparisons Between Centrality Measures in Network Modelshttps://sciendo.com/article/10-21307/joss-2024-001<abstract> <title style='display:none'>Abstract</title> <p>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.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10-21307/joss-2024-0012024-01-20T00:00:00.000+00:00Longitudinal Network Modelshttps://sciendo.com/article/10.21307/joss-2023-005ARTICLEtruehttps://sciendo.com/article/10.21307/joss-2023-0052023-11-20T00:00:00.000+00:00A Research Agenda for Social Networks and Social Resiliencehttps://sciendo.com/article/10.21307/joss-2023-004ARTICLEtruehttps://sciendo.com/article/10.21307/joss-2023-0042023-10-31T00:00:00.000+00:00Personal Networks: Classic Readings and New Directions in Egocentric Analysishttps://sciendo.com/article/10.21307/joss-2023-003ARTICLEtruehttps://sciendo.com/article/10.21307/joss-2023-0032023-10-26T00:00:00.000+00:00Trade and Nation: How Companies and Politics Reshaped Economic Thoughthttps://sciendo.com/article/10.21307/joss-2023-002ARTICLEtruehttps://sciendo.com/article/10.21307/joss-2023-0022023-09-13T00:00:00.000+00:00Living in Networks: The Dynamics of Social Relationshttps://sciendo.com/article/10.21307/joss-2023-001ARTICLEtruehttps://sciendo.com/article/10.21307/joss-2023-0012023-05-02T00:00:00.000+00:00Syndicate Women: Gender and Networks in Chicago Organized Crimehttps://sciendo.com/article/10.21307/joss-2022-001ARTICLEtruehttps://sciendo.com/article/10.21307/joss-2022-0012022-04-21T00:00:00.000+00:00An Analysis of Relations Among European Countries Based on UEFA European Football Championshiphttps://sciendo.com/article/10-21307/joss-2022-004<abstract> <title style='display:none'>Abstract</title> <p>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.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10-21307/joss-2022-0042022-08-14T00:00:00.000+00:00An Analysis of Relations Among European Countries Based on UEFA European Football Championshiphttps://sciendo.com/article/10.21307/joss-2022-004<abstract> <title style='display:none'>Abstract</title> <p>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.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.21307/joss-2022-0042022-08-14T00:00:00.000+00:00A Network Analysis of Twitter's Crackdown on the QAnon Conversationhttps://sciendo.com/article/10.21307/joss-2022-002<abstract> <title style='display:none'>Abstract</title> <p>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.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.21307/joss-2022-0022022-05-16T00:00:00.000+00:00Social Networks of Meaning and Communicationhttps://sciendo.com/article/10.21307/joss-2022-006ARTICLEtruehttps://sciendo.com/article/10.21307/joss-2022-0062022-10-09T00:00:00.000+00:00Advances in Network Clustering and Blockmodelinghttps://sciendo.com/article/10.21307/joss-2022-005ARTICLEtruehttps://sciendo.com/article/10.21307/joss-2022-0052022-08-31T00:00:00.000+00:00Inferential Network Analysishttps://sciendo.com/article/10.21307/joss-2022-003ARTICLEtruehttps://sciendo.com/article/10.21307/joss-2022-0032022-06-02T00:00:00.000+00:00Book Review: https://sciendo.com/article/10.21307/joss-2021-002ARTICLEtruehttps://sciendo.com/article/10.21307/joss-2021-0022021-11-24T00:00:00.000+00:00Book Review: https://sciendo.com/article/10.21307/joss-2021-001ARTICLEtruehttps://sciendo.com/article/10.21307/joss-2021-0012021-09-17T00:00:00.000+00:00A Longitudinal Network Analysis of the German Knowledge Economy from 2009 to 2019: Spatio-Temporal Dynamics at the City–Firm Nexushttps://sciendo.com/article/10.21307/joss-2020-005<abstract> <title style='display:none'>Abstract</title> <p>Multi-location knowledge-intensive firms span their value chains and thus their locations across space. Increased globalization alters the spatial configuration of such networks of knowledge creation. Longitudinal social network analysis allows detecting temporal changes in the arrangement of nodes and edges in the network and resulting changes in the overall structure. We use this approach to study for Germany the spatio-temporal dynamics of knowledge-intensive services firms – advanced producer services (APS) – in the years between 2009 and 2019. Multi-location APS firms are considered as vanguard of spatial structural change and thus lending to study their location choice behavior. A common approach is to analyze a one-mode intercity network where cities are the nodes. We take a different approach and include the firms’ perspectives. We work directly with the original data structure of a two-mode network including cities and firms as two node sets and we apply stochastic actor-oriented models for network dynamics. Results show that the spatio-temporal dynamics are characterized by both agglomeration and network economies. On a local scale, APS firms continue their location expansion over time and concentrate in agglomerations where many other APS firms and a greater availability of workforce are present. Simultaneously, they also choose new locations in agglomerations further apart from their present locations. On a supra-local scale, the network grows denser over time. Agglomerations that are attractive for APS firms in 2009 become even more attractive in 2019. Our analysis contributes to an understanding of how interactions amongst cities and firms on a local scale give rise to the empirically observed network patterns on a supra-local scale.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.21307/joss-2020-0052020-11-25T00:00:00.000+00:00Geodesic Cycle Length Distributions in Delusional and Other Social Networkshttps://sciendo.com/article/10.21307/joss-2020-002<abstract> <title style='display:none'>Abstract</title> <p>A recently published paper [Martin (2017) JoSS 18(1):1-21] investigates the structure of an unusual set of social networks, those of the alternate personalities described by a patient undergoing therapy for multiple personality disorder (now known as dissociative identity disorder). The structure of these networks is modeled using the <italic>dk</italic>-series, a sequence of nested network distributions of increasing complexity. Martin finds that the first of these networks contains a striking feature of a large “hollow ring”; a cycle with no shortcuts, so that the shortest path between any two nodes in the cycle is along the cycle (in more precise graph theory terms, this is a geodesic cycle). However, the subsequent networks have much smaller largest cycles, smaller than those expected by the models. In this work, I re-analyze these delusional social networks using exponential random graph models (ERGMs) and investigate the distribution of the lengths of geodesic cycles. I also conduct similar investigations for some other social networks, both fictional and empirical, and show that the geodesic cycle length distribution is a macro-level structure that can arise naturally from the micro-level processes modeled by the ERGM.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.21307/joss-2020-0022020-10-01T00:00:00.000+00:00Comment on Geodesic Cycle Length Distributions in Delusional and Other Social Networkshttps://sciendo.com/article/10.21307/joss-2020-003ARTICLEtruehttps://sciendo.com/article/10.21307/joss-2020-0032020-10-01T00:00:00.000+00:00Reaching for Unique Resources: Structural Holes and Specialization in Scientific Collaboration Networkshttps://sciendo.com/article/10.21307/joss-2020-001<abstract> <title style='display:none'>Abstract</title> <p>On some fundamental level, we can think of scholars as actors possessing, or controlling, various types of resources. Collaboration in science is understood here as a process of pooling and exchanging such resources. We show how diversity of resources engaged in scientific collaboration is related to the structure of collaboration networks. We demonstrate that scholars within their personal networks simultaneously (1) diversify resources in collaboration ties surrounded by structural holes and (2) specialize resources in collaboration ties embedded in dense collaboration groups. These complementary mechanisms decrease individual efforts required to maintain effective collaborations in complex social settings. To this end, we develop a concept of “pairwise redundancy” capturing structural redundancy of ego’s neighbors <italic>vis-à-vis</italic> each other.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.21307/joss-2020-0012020-07-30T00:00:00.000+00:00Reply to “Comment on Geodesic Cycle Length Distributions in Delusional and Other Social Networks”https://sciendo.com/article/10.21307/joss-2020-004<abstract> <title style='display:none'>Abstract</title> <p>Martin (2020) describes a misinterpretation of exponential random graph (ERGM) parameters in my contribution (Stivala 2020), with the use of this parametric model obscuring, rather than illuminating, the data. He suggests that this is symptomatic of a trend in the social networks community towards a methodological monoculture focussed on the use of ERGMs. In this Reply I try to clarify how this situation arose in this specific case, and address some more general issues Martin raises, including the use of nodal covariates, what we can learn from ERGMs, and methodological monoculturalism in social network research.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.21307/joss-2020-0042020-10-01T00:00:00.000+00:00en-us-1