Community Detection in Dynamic Networks via Adaptive Label Propagation

An adaptive label propagation algorithm (ALPA) is proposed to detect and monitor communities in dynamic networks. Unlike the traditional methods by re-computing the whole community decomposition after each modification of the network, ALPA takes into account the information of historical communities and updates its solution according to the network modifications via a local label propagation process, which generally affects only a small portion of the network. This makes it respond to network changes at low computational cost. The effectiveness of ALPA has been tested on both synthetic and real-world networks, which shows that it can successfully identify and track dynamic communities. Moreover, ALPA could detect communities with high quality and accuracy compared to other methods. Therefore, being low-complexity and parameter-free, ALPA is a scalable and promising solution for some real-world applications of community detection in dynamic networks.Comment: 16 pages, 11 figure

An Agent Based Simulation of Smart Metering Technology Adoption

Based on the classic behavioural theory “the Theory of Planned Behaviour”, we develop an agent-based model to simulate the diffusion of smart metering technology in the electricity market. We simulate the emergent adoption of smart metering technology under different management strategies and economic regulations. Our research results show that in terms of boosting the take-off of smart meters in the electricity market, choosing the initial users on a random and geographically dispersed basis and encouraging meter competition between energy suppliers can be two very effective strategies. We also observe an “S-curve” diffusion of smart metering technology and a “lock-in” effect in the model. The research results provide us with insights as to effective policies and strategies for the roll-out of smart metering technology in the electricity market

Disentangling urban sustainability: the Flemish City Monitor acknowledges complexity

Nowadays, cities have to deal with complexity. In this article we argue that the City Monitor for Sustainable Urban Development in the Flanders (Belgium) acknowledges complexity. This set of almost 200 SDIs (Sustainable Development Indicators) contains actor-exceeding and policy-exogenous information. On that account this learning instrument is relevant for all actors involved in the urban (sustainable) development of their city and is able to enhance and to sharpen the quality of strategic urban debates and complex decision-making processes. Our intensive co-design approach of the City Monitor also succeeds to deal adequate with the tensions of complex catch-all terms such as (urban) sustainability

The Transition Town Network: a review of current evolutions and renaissance

The Transition Network started as a movement with Transition Totnes (Devon, UK) in late 2005, with Rob Hopkins as its founder. To date it has grown to encompass 313 official Transition Network initiatives spread across the world from the UK (with roughly 50% of all initiatives) to the USA, Canada, Italy, Japan, Germany, Ireland, New Zealand, Chile, the Netherlands, Brazil and so on (Transition Network, 2010a). For any social movement, this could most certainly be described as something of a success and warrants a closer examination. Indeed, the aim of this profile is to explore the movement's aims and modus operandi, the problematics it has faced and how it is now evolving. The profile draws on my auto-ethnographic encounters with the movement in Transition Nottingham and at the recent Transition Network Conference 2010, whilst also being grounded in the material made publically available on the Transition Network and Transition Culture websites (see Transition Network, 2010b and Transition Culture, 2010a)

Benchmark model to assess community structure in evolving networks

Detecting the time evolution of the community structure of networks is crucial to identify major changes in the internal organization of many complex systems, which may undergo important endogenous or exogenous events. This analysis can be done in two ways: considering each snapshot as an independent community detection problem or taking into account the whole evolution of the network. In the first case, one can apply static methods on the temporal snapshots, which correspond to configurations of the system in short time windows, and match afterwards the communities across layers. Alternatively, one can develop dedicated dynamic procedures, so that multiple snapshots are simultaneously taken into account while detecting communities, which allows us to keep memory of the flow. To check how well a method of any kind could capture the evolution of communities, suitable benchmarks are needed. Here we propose a model for generating simple dynamic benchmark graphs, based on stochastic block models. In them, the time evolution consists of a periodic oscillation of the system's structure between configurations with built-in community structure. We also propose the extension of quality comparison indices to the dynamic scenario.Comment: 11 pages, 7 figures, 3 table