Email Communication Network Research Articles

Rich-club and page-club coefficients for directed graphs

Rich-club and page-club coefficients and their null models are introduced for directed graphs. Null models allow for a quantitative discussion of the rich-club and page-club phenomena. These coefficients are computed for four directed real-world networks: Arxiv High Energy Physics paper citation network, Web network (released from Google), Citation network among US Patents, and email network from a EU research institution. The results show a high correlation between rich-club and page-club ordering. For journal paper citation network, we identify both rich-club and page-club ordering, showing that “elite” papers are cited by other “elite” papers. Google web network shows partial rich-club and page-club ordering up to some point and then a narrow declining of the corresponding normalized coefficients, indicating the lack of rich-club ordering and the lack of page-club ordering, i.e. high in-degree (PageRank) pages purposely avoid sharing links with other high in-degree (PageRank) pages. For UC patents citation network, we identify page-club and rich-club ordering providing a conclusion that “elite” patents are cited by other “elite” patents. Finally, for email communication network we show lack of both rich-club and page-club ordering. We construct an example of synthetic network showing page-club ordering and the lack of rich-club ordering.

Dynamic organizations. How to measure evolution and change in organizations by analyzing email communication networks

Since organizations are dynamic systems, they change over time. Communication within organizations represents these changes. The use of social network analysis is an established (although still not common) approach to better understanding organizational structures. Methods and business applications for dynamic analyses are still a new, though promising field. The increasing use of computer mediated communication, like email, provides particularly relevant data for studying organizations. Focusing exclusively on the changes in the use of email communication over time, two different methodological approaches are used to analyze the email data stream: descriptive network visualizations and a new model using exponential random graph models for event stream data. In a simulation with 97 students, a merger of two different organizations was replicated.

Reflection of @SCAD.edu - A virtual mirror on E-mail communication at an art and design university

This article describes an information system visualizing the E-mail communication of students and staff at the Savannah College of Art and Design (SCAD). In contrast to other visualization tools the described software is to be exhibited in a public space without the possibility of sophisticated user interaction. In such a setting addressing privacy issues is a key factor to the acceptance of the software. Besides visualizing the E-mail communication network the system also takes a look at the social media service Twitter to gain an impression on the discussed topic in the gathered E-mails.

Semiconducting properties of pyrite

The stochastic block model (SBM) is a popular model for capturing community structure and interaction within a network. Network data with non-Boolean edge weights is becoming commonplace; however, existing analysis methods convert such data to a binary representation to apply the SBM, leading to a loss of information. A generalisation of the SBM is considered, which allows edge weights to be modelled in their recorded state. An effective reversible jump Markov chain Monte Carlo sampler is proposed for estimating the parameters and the number of blocks for this generalised SBM. The methodology permits non-conjugate distributions for edge weights, which enable more flexible modelling than current methods as illustrated on synthetic data, a network of brain activity and an email communication network.