Ian McCulloh

Social networks and spatial configuration—How office layouts drive social interaction

Abstract This paper analyzes the spatial dimensions of office layouts in diverse knowledge-intensive workplace environments based on the theoretical and methodological propositions of Space Syntax, and brings this together with the analysis of intra-organizational interaction networks. Physical distances between agents are modeled in different ways and used as explanatory variables in exponential random graph modeling. The paper shows that spatial configuration in offices can be considered an important but not sole rationale for tie formation. Furthermore, it is shown that spatial distance measures based on detailed configurational analysis outperform simple Euclidean distance metrics in predicting social ties.

Social Network Change Detection

Changes in observed social networks may signal an underlying change within an organization, and may even predict significant events or behaviors. The breakdown of a team’s effectiveness, the emergence of informal leaders, or the preparation of an attack by a clandestine network may all be associated with changes in the patterns of interactions between group members. The ability to systematically, statistically, effectively and efficiently detect these changes has the potential to enable the anticipation of change, provide early warning of change, and enable faster response to change. By applying statistical process control techniques to social networks we can detect changes in these networks. Herein we describe this methodology and then illustrate it using three data sets. The first deals with the email communications among graduate students. The second is the perceived connections among members of al Qaeda based on open source data. The results indicate that this approach is able to detect change even with the high levels of uncertainty inherent in these data.

Spectral analysis of social networks to identify periodicity

Two key problems in the study of longitudinal networks are determining when to chunk continuous time data into discrete time periods for network analysis and identifying periodicity in the data. In addition, statistical process control applied to longitudinal social network measures can be biased by the effects of relational dependence and periodicity in the data. Thus, the detection of change is often obscured by random noise. Fourier analysis is used to determine statistically significant periodic frequencies in longitudinal network data. Two approaches are then offered: using significant periods as a basis to chunk data for longitudinal network analysis or using the significant periods to filter the longitudinal data. E-mail communication collected at the United States Military Academy is examined.

Towards supply chain excellence using network analysis

The literature suggests a growing interest in the application of network analysis in supply chain management. However this has been at the organizational rather than the process level. We believe there is value in applying such analysis to internal processes in supply chain networks. This study uses network analysis techniques to investigate the Stewart [8] framework for excellence in supply chains which considers the impact of delivery performance, flexibility and responsiveness, logistics cost and assets management. We analyze traditional process flow charts as a network in which nodes represent processes, and links between processes have values associated with dimensions of excellence. The supply chain is analyzed and viewed using measures of centrality and clustering related to the dimensions of excellence, and compared with traditional perceptions of the same processes which are more related to time criticality. Overall, the study indicates that traditional means of managing supply chains are not only unfocused in terms of excellence, but are compromised through inability to recognize the importance of various process groups, with a serious mismatch of resourcing as a result. The study provides early findings supporting the future development of a methodology to better manage supply chains using network analysis, in particular to better prioritize resources on critical processes.