Classical Social Network Research Articles

A new clustering method and its application in social networks

In a graph theory model, clustering is the process of division of vertices into groups, with a higher density of edges within groups than between them. In this paper, we introduce a new clustering method for detecting such groups and use it to analyse some classic social networks. The new method has two distinguished features: non-binary hierarchical tree and the feature of overlapping clustering. A non-binary hierarchical tree is much smaller than the binary-trees constructed by most traditional methods and, therefore, it clearly highlights meaningful clusters which significantly reduces further manual efforts for cluster selections. The present method is tested by several bench mark data sets for which the community structure was known beforehand and the results indicate that it is a sensitive and accurate method for extracting community structure from social networks.

ANALYSIS OF COGNITIVE SOCIAL AND KNOWLEDGE NETWORKS FROM ELECTRONIC COMMUNICATION

This paper explains the classical social network analysis and discusses how computer networks effect a shift in constructing social networks. The paper then concentrates on analyzing cognitive aspects of a social network, explaining a simple but scalable approach for modeling a socio-cognitive network. Novel measures using such a socio-cognitive network model are defined and applications of such measures to extract useful information is illustrated on the Enron email dataset. The paper then describes a Dempster-Schafer theory based approach towards modeling a cognitive knowledge network and uses the Enron email dataset to illustrate how the proposed model can be used to capture actors' perceptions in a knowledge network. The paper concludes with a summary of the proposed models and a discussion on new research directions that can arise due to such cognitive analyses of electronic communication data.

Size Really Matters—New Insights for Start-ups’ Survival

This paper presents new evidence regarding a firm’s probability for survival, based on the network structure of the firm’s managers. We found that start-ups that have larger informal communication networks increased their chance to survive external shock. Original data have been collected from Israeli software start-ups during the dot-com economic growth. About eight years later, we added information about their ability to survive the burst of the dot-com bubble. From a theoretical point of view, this paper highlights the power of the classic social networks approach in explaining organizational performance. From a practical point of view, these findings offer some guidelines for managers of start-ups. Our results show that the size of informal interfirm networks really matters.

A Multivariate Test of the Bott Hypothesis in an Urban Irish Setting

In her classic study, Family and Social Network, Bott (1971) argued that the connectedness of a married person's social network was negatively related to his/her marital integration. Using a sample of 686 married Irish women in Cork City this hypothesis was tested, and the results of a multivariate regression analysis revealed that neither network connectedness nor the strength of the respondent's emotional ties to the network had any explanatory power. The two variables that accounted for most of the 29 percent explained variance were the number of wife's network members shared by her husband and SES. A reconceptualization of the Bott hypothesis incorporating these findings, with networks seen as compensatory rather than mediating mechanisms, is oftered.