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

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.

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.

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.

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.

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.