<abstract xmlns="http://www.w3.org/1999/xhtml">

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.
</abstract>

Reaching for Unique Resources: Structural Holes and Specialization in Scientific Collaboration Networks

<abstract xmlns="http://www.w3.org/1999/xhtml">

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.
</abstract>

Geodesic Cycle Length Distributions in Delusional and Other Social Networks

Comment on Geodesic Cycle Length Distributions in Delusional and Other Social Networks

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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.
</abstract>

Reply to “Comment on Geodesic Cycle Length Distributions in Delusional and Other Social Networks”

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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.
</abstract>

A Longitudinal Network Analysis of the German Knowledge Economy from 2009 to 2019: Spatio-Temporal Dynamics at the City–Firm Nexus

AHEAD OF PRINT

Volume 25 (2024): Issue 1 (January 2024)

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Volume 18 (2017): Issue 1 (January 2017)

Volume 17 (2016): Issue 1 (January 2016)

Volume 16 (2015): Issue 1 (January 2015)

Volume 15 (2014): Issue 1 (January 2014)

Volume 14 (2013): Issue 1 (January 2013)

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Volume 10 (2009): Issue 1 (January 2009)

Journal of Social Structure

The Journal of Social Structure (JoSS) is an electronic journal of the International Network for Social Network Analysis (INSNA). It is designed to facilitate timely dissemination of state-of-the-art results in the interdisciplinary research area of the network representation of social structure. It publishes empirical, theoretical and methodological articles focused on social networks and their analysis.  JoSS is especially interested in manuscripts that are focused on social structure in network terms, that is, in terms of the patterning of social linkages among actors. These actors could be comprised of different types or levels of analysis, such as animals, humans, artificial agents, groups or organizations.  Articles are accepted only after peer review. Comments, rejoinders and extensions on already published articles will be peer-reviewed and if accepted will be attached via a hyperlink to the previously published piece.   Open Access Policy  This journal provides immediate open access to its content under the Creative Commons CC BY-NC 4.0 license on the principle that making research freely available to the public supports a greater global exchange of knowledge. Under the CC BY-NC 4.0 license users are free to share (copy and redistribute the material in any medium or format) and adapt the work (remix, transform, and build upon the material for any purpose) if the contribution was properly attributed and and used for non-commercial purposes.  ABOUT SOCIETY  International Network for Social Network Analysis is the professional association for researchers interested in social network analysis. The association is a non-profit organization incorporated in the state of Delaware and founded by Barry Wellman in 1977. INSNA was founded on the premise that the behavior and lives of social entities are affected by their position in the overall social structure. Network analysts believe that how an individual lives depends in large part on how that individual is tied into the larger web of social connections. Many believe, moreover, that the success or failure of societies and organizations often depends on the patterning of their internal structure. Social network analysis has found important applications in organizational behavior, inter-organizational relations, the spread of contagious diseases, mental health, social support, the diffusion of information and animal social organization. Today it has become an international effort with its own professional organizations, textbooks, journals, research centers, training centers and computer programs designed specifically to facilitate the analysis of social network structural data.   Archiving  Sciendo archives the contents of this journal in Portico - digital long-term preservation service of scholarly books, journals and collections.  Plagiarism Policy  The editorial board is participating in a growing community of Similarity Check System's users in order to ensure that the content published is original and trustworthy. Similarity Check is a medium that allows for comprehensive manuscripts screening, aimed to eliminate plagiarism and provide a high standard and quality peer-review process. 