Fast Multi-Scale Community Detection based on Local Criteria within a Multi-Threaded Algorithm

Many systems can be described using graphs, or networks. Detecting communities in these networks can provide information about the underlying structure and functioning of the original systems. Yet this detection is a complex task and a large amount of work was dedicated to it in the past decade. One important feature is that communities can be found at several scales, or levels of resolution, indicating several levels of organisations. Therefore solutions to the community structure may not be unique. Also networks tend to be large and hence require efficient processing. In this work, we present a new algorithm for the fast detection of communities across scales using a local criterion. We exploit the local aspect of the criterion to enable parallel computation and improve the algorithm's efficiency further. The algorithm is tested against large generated multi-scale networks and experiments demonstrate its efficiency and accuracy.Comment: arXiv admin note: text overlap with arXiv:1204.100

Accurator: Nichesourcing for Cultural Heritage

With more and more cultural heritage data being published online, their usefulness in this open context depends on the quality and diversity of descriptive metadata for collection objects. In many cases, existing metadata is not adequate for a variety of retrieval and research tasks and more specific annotations are necessary. However, eliciting such annotations is a challenge since it often requires domain-specific knowledge. Where crowdsourcing can be successfully used for eliciting simple annotations, identifying people with the required expertise might prove troublesome for tasks requiring more complex or domain-specific knowledge. Nichesourcing addresses this problem, by tapping into the expert knowledge available in niche communities. This paper presents Accurator, a methodology for conducting nichesourcing campaigns for cultural heritage institutions, by addressing communities, organizing events and tailoring a web-based annotation tool to a domain of choice. The contribution of this paper is threefold: 1) a nichesourcing methodology, 2) an annotation tool for experts and 3) validation of the methodology and tool in three case studies. The three domains of the case studies are birds on art, bible prints and fashion images. We compare the quality and quantity of obtained annotations in the three case studies, showing that the nichesourcing methodology in combination with the image annotation tool can be used to collect high quality annotations in a variety of domains and annotation tasks. A user evaluation indicates the tool is suited and usable for domain specific annotation tasks

Towards a classification framework for social machines

The state of the art in human interaction with computational systems blurs the line between computations performed by machine logic and algorithms, and those that result from input by humans, arising from their own psychological processes and life experience. Current socio-technical systems, known as ‘social machines’ exploit the large-scale interaction of humans with machines. Interactions that are motivated by numerous goals and purposes including financial gain, charitable aid, and simply for fun. In this paper we explore the landscape of social machines, both past and present, with the aim of defining an initial classificatory framework. Through a number of knowledge elicitation and refinement exercises we have identified the polyarchical relationship between infrastructure, social machines, and large-scale social initiatives. Our initial framework describes classification constructs in the areas of contributions, participants, and motivation. We present an initial characterization of some of the most popular social machines, as demonstration of the use of the identified constructs. We believe that it is important to undertake an analysis of the behaviour and phenomenology of social machines, and of their growth and evolution over time. Our future work will seek to elicit additional opinions, classifications and validation from a wider audience, to produce a comprehensive framework for the description, analysis and comparison of social machines