Network Churn: The Effects of Self-Monitoring Personality on Brokerage Dynamics

The apparent stability of social network structures may mask considerable change and adjustment in the ties that make up the structures. In this study, we theorize and test-using longitudinal data on friendship relations from a radiology department located in the Netherlands-the idea that the characteristics of this "network churn" and the resultant brokerage dynamics are traceable to individual differences in self-monitoring personality. High self-monitors were more likely than low self-monitors to attract new friends and to occupy new bridging positions over time. In comparison to low self-monitors, the new friends that high self-monitors attracted tended to be relative strangers, in the sense that they were unconnected with previous friends, came from different functions, and more efficiently increased the number of structural holes in the resultant network. Our study suggests that dispositional forces help shape the dynamic structuring of networks: individuals help (re)create the social network structures they inhabit. © 2010 by Johnson Graduate School

The question of EU legitimacy in the Social OMC peer review process

This paper examines the structural kand relational factors that affect perceptions of legitimacy in the EU's Social Open Method of Coordination, particularly in its peer review process. Using multi-level governance as its analytical framework, the paper uses network analysis to examine the actor networks and relations within the peer review process over time, and links this with different conceptions of input and throughput legitimacy, and the findings show that the peer reviews do not significantly feed into either input or throughput legitimacy. While input legitimacy is improved somewhat by the inclusion of additional actors in the policy process, these actors do not have a clear role to play and the peer reviews remain driven by traditional actors. In terms of throughput, the process does positively address issues of transparency, procedure and information provision, there is no clear path for translating these processes into an increase in perceptions of legitimacy

Correlation between centrality metrics and their application to the opinion model

In recent decades, a number of centrality metrics describing network properties of nodes have been proposed to rank the importance of nodes. In order to understand the correlations between centrality metrics and to approximate a high-complexity centrality metric by a strongly correlated low-complexity metric, we first study the correlation between centrality metrics in terms of their Pearson correlation coefficient and their similarity in ranking of nodes. In addition to considering the widely used centrality metrics, we introduce a new centrality measure, the degree mass. The m order degree mass of a node is the sum of the weighted degree of the node and its neighbors no further than m hops away. We find that the B_{n}, the closeness, and the components of x_{1} are strongly correlated with the degree, the 1st-order degree mass and the 2nd-order degree mass, respectively, in both network models and real-world networks. We then theoretically prove that the Pearson correlation coefficient between x_{1} and the 2nd-order degree mass is larger than that between x_{1} and a lower order degree mass. Finally, we investigate the effect of the inflexible antagonists selected based on different centrality metrics in helping one opinion to compete with another in the inflexible antagonists opinion model. Interestingly, we find that selecting the inflexible antagonists based on the leverage, the B_{n}, or the degree is more effective in opinion-competition than using other centrality metrics in all types of networks. This observation is supported by our previous observations, i.e., that there is a strong linear correlation between the degree and the B_{n}, as well as a high centrality similarity between the leverage and the degree.Comment: 20 page

Social Cohesion, Structural Holes, and a Tale of Two Measures

EMBARGOED - author can archive pre-print or post-print on any open access repository after 12 months from publication. Publication date is May 2013 so embargoed until May 2014.This is an author’s accepted manuscript (deposited at arXiv arXiv:1211.0719v2 [physics.soc-ph] ), which was subsequently published in Journal of Statistical Physics May 2013, Volume 151, Issue 3-4, pp 745-764. The final publication is available at link.springer.com http://link.springer.com/article/10.1007/s10955-013-0722-

Changing Faces: Identifying Complex Behavioural Profiles

There has been significant interest in the identification and profiling of insider threats, attracting high-profile policy focus and strategic research funding from governments and funding bodies. Recent examples attracting worldwide attention include the cases of Chelsea Manning, Edward Snowden and the US authorities. The challenges with profiling an individual across a range of activities is that their data footprint will legitimately vary significantly based on time and/or location. The insider threat problem is thus a specific instance of the more general problem of profiling complex behaviours. In this paper, we discuss our preliminary research models relating to profiling complex behaviours and present a set of experiments related to changing roles as viewed through large-scale social network datasets, such as Twitter. We employ psycholinguistic metrics in this work, considering changing roles from the standpoint of a trait-based personality theory. We also present further representations, including an alternative psychological theory (not trait-based), and established techniques for crime modelling, spatio-temporal and graph/network, to investigate within a wider reasoning framework