Critical Nodes In Directed Networks

Critical nodes—or “middlemen”—have an essential place in both social and economic networks when considering the flow of information and trade. This paper extends the concept of critical nodes to directed networks and in doing so identify strong and weak middlemen.
Node contestability is introduced as a form of competition in networks; a duality between uncontested intermediaries and middlemen is established. The brokerage power of middlemen is formally expressed and a general algorithm is constructed to measure the brokerage power of each node from the networks adjacency matrix. Augmentations of the brokerage power measure are discussed to encapsulate relevant centrality measures. Furthermore, we extend these notions and provide measures of the competitiveness of a network.
We use these concepts to identify and measure middlemen in two empirical socio-economic networks, the elite marriage network of Renaissance Florence and Krackhardt’s advice net- work.

Sir Robert Hart Photo Collection on Flickr

The photographs in this album were selected with the assistance of the Sir Robert Hart Research Project, which is a collaboration between Special Collections & Archives in the Library, the School of Modern History & Anthropology, Queen’s University Belfast, and the Institute of Modern History at the Chinese Academy of Social Sciences (CASS) in Beijing. The research project is creating an annotated photobook from the Sir Robert Hart Photo Collection (originally donated in the 1970s) and the Irons Collection. The photographs here reflect those that will be included in the book.

Prying into the intimate secrets of animal lives; software beyond hardware for comprehensive annotation in 'Daily Diary' tags

BACKGROUND: Smart tags attached to freely-roaming animals recording multiple parameters at infra-second rates are becoming commonplace, and are transforming our understanding of the way wild animals behave. Interpretation of such data is complex and currently limits the ability of biologists to realise the value of their recorded information.

DESCRIPTION: This work presents Framework4, an all-encompassing software suite which operates on smart sensor data to determine the 4 key elements considered pivotal for movement analysis from such tags (Endangered Species Res 4: 123-37, 2008). These are; animal trajectory, behaviour, energy expenditure and quantification of the environment in which the animal moves. The program transforms smart sensor data into dead-reckoned movements, template-matched behaviours, dynamic body acceleration-derived energetics and position-linked environmental data before outputting it all into a single file. Biologists are thus left with a single data set where animal actions and environmental conditions can be linked across time and space.

CONCLUSIONS: Framework4 is a user-friendly software that assists biologists in elucidating 4 key aspects of wild animal ecology using data derived from tags with multiple sensors recording at high rates. Its use should enhance the ability of biologists to derive meaningful data rapidly from complex data.