<abstract xmlns="http://www.w3.org/1999/xhtml">The main aim of this study is twofold: first, to examine the underlying structure of coauthorship in Indian economics; and second, to explore the link between the participation in scientific collaborations and academic visibility. We decipher the structure of co-authorship by presenting collaboration networks of scholars who published articles in six Indian economics journals during 1966-2005, which is split into four windows: 1966-75, 1976-85, 1986-95, and 1996-2005. In this study, the following social network measures are applied: the size of the network, the size of the main component, average degree, path length, and clustering coefficient. The study presents the following three features of Indian economics: first, a substantial proportion of Indian authors are isolated, albeit declining very slowly over a period of time; second, it appears that the structure of scholarly collaboration in Indian economics is highly fragmented, and the observed size of main components accounts for a small proportion of the total authors; third, and more importantly, the size and composition of co-authorship networks presented in the paper seldom impact the scientific visibility of authors.</abstract>

The Structure of Collaboration Networks: An Illustration of Indian Economics

<abstract xmlns="http://www.w3.org/1999/xhtml">As human beings, we understand and make sense of the social world using social cognition. Social cognitions are cognitive processes through which we understand, process, and recall our interactions with others. Most agent-based models do not account for social cognition; rather, they either provide detailed models of task-related cognition or model many actors and focus on social processes. In general, the more cognitively realistic the models, the less they explain human social behavior and the more computationally expensive it is to model a single agent. In contrast, in this research an agent-based model containing an explicit model of social cognition is developed. Results from this model demonstrate that adding social cognition both improves the model veridicality and decreases computation costs.</abstract>

The Power of Social Cognition

<abstract xmlns="http://www.w3.org/1999/xhtml">Among the many centrality indices used to detect structures of actors’ positions in networks is the use of the first eigenvector of an adjacency matrix that captures the connections among the actors. This research considers the seeming pervasive current practice of using only the first eigenvector. It is shows that, as in other statistical applications of eigenvectors, subsequent vectors can also contain illuminating information. Several small examples, and Freeman’s EIES network, are used to illustrate that while the first eigenvector is certainly informative, the second (and subsequent) eigenvector(s) can also be equally tractable and informative.</abstract>

Eigenvector Centrality: Illustrations Supporting the Utility of Extracting More Than One Eigenvector to Obtain Additional Insights into Networks and Interdependent Structures

<abstract xmlns="http://www.w3.org/1999/xhtml">This study analyzed the e-mail exchange network of participants of a national dating website. The investigation examined whether aggregated partner preferences give rise to distinct, “invisible,” clusters in online dating networks that structure dating opportunities and result in homophilous subgroups. The findings identified and visualized the ten largest network clusters of participants who interacted with each other and examined the dater characteristics most responsible for cluster membership. Rated attractiveness and age were the strongest cluster correlates, whereas education and race were relatively uncommon determinants. In sum, daters’ interdependent actions created aggregate communities unseen by the users themselves, but no less influential for dating opportunities, that were based more on attractiveness and age than on race and education.</abstract>

The Invisible Contours of Online Dating Communities: A Social Network Perspective

<abstract xmlns="http://www.w3.org/1999/xhtml">A rare set of data on a changing social network of personalities, drawn by a sufferer of Multiple Personality Disorder are investigated using random graph theory. The key features guiding the patient’s production of these wholly delusional networks, features which define her “schema” of social network, are derived by fitting a family of nested distributions. From this, we can derive a tentative hypothesis of how the laity may understand the logic of social networks, a hypothesis that is consonant with other forms of informal evidence.</abstract>

The Structure of Node and Edge Generation in a Delusional Social Network

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

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Volume 23 (2022): Issue 1 (March 2022)

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Volume 21 (2020): Issue 1 (January 2020)

Volume 20 (2019): Issue 4 (January 2019)

Volume 20 (2019): Issue 3 (January 2019)

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Volume 20 (2019): Issue 1 (January 2019)

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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)

Volume 13 (2012): Issue 1 (January 2012)

Volume 12 (2011): Issue 1 (January 2011)

Volume 11 (2010): Issue 1 (January 2010)

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. 