a b s t r a c tCommunities can intuitively be defined as subsets of nodes of a graph with a dense structure in the corresponding subgraph. However, for mining such communities usually only structural aspects are taken into account. Typically, no concise nor easily interpretable community description is provided.For tackling this issue, this paper focuses on description-oriented community detection using subgroup discovery. In order to provide both structurally valid and interpretable communities we utilize the graph structure as well as additional descriptive features of the graph's nodes. A descriptive community pattern built upon these features then describes and identifies a community, i.e., a set of nodes, and vice versa. Essentially, we mine patterns in the ''description space'' characterizing interesting sets of nodes (i.e., subgroups) in the ''graph space''; the interestingness of a community is evaluated by a selectable quality measure.We aim at identifying communities according to standard community quality measures, while providing characteristic descriptions of these communities at the same time. For this task, we propose several optimistic estimates of standard community quality functions to be used for efficient pruning of the search space in an exhaustive branch-and-bound algorithm. We demonstrate our approach in an evaluation using five real-world data sets, obtained from three different social media applications.

Abstract. This paper focuses on the community analysis of conference participants using their face-to-face contacts, visited talks, and tracks in a social and ubiquitous conferencing scenario. We consider human face-to-face contacts and perform a dynamic analysis of the number of contacts and their lengths. On these dimensions, we specifically investigate user-interaction and community structure according to different special interest groups during a conference. Additionally, using the community information, we examine different roles and their characteristic elements. The analysis is grounded using real-world conference data capturing community information about participants and their face-to-face contacts. The analysis results indicate, that the face-to-face contacts show inherent community structure grounded using the special interest groups. Furthermore, we provide individual and community-level properties, traces of different behavioral patterns, and characteristic (role) profiles.

Conferator is a novel social conference system that provides the management of social interactions and context information in ubiquitous and social environments. Using RFID and social networking technology, Conferator provides the means for effective management of personal contacts and according conference information before, during and after a conference. We describe the system in detail, before we analyze and discuss results of a typical application of the Conferator system.Zusammenfassung Als ein neuartiges soziales Konferenzmanagementsystem ermöglicht der Conferator die einfache Verwaltung sozialer Beziehungen und Interaktionen sowie das Management von konferenzspezifischen Informationen sowohl vor, während als auch nach einer Konferenz. Basierend auf RFID Technik gekoppelt mit sozialen Netzen bietet der Conferator die Möglichkeit, einfach und effektiv persönliche Kontakte und Informationen wie etwa den Konferenzplan zu verwalten. Wir beschreiben das System und präsentieren Analyseergebnisse in einem typischen Konferenz-Anwendungsszenario.

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Since the rise of collaborative tagging systems on the web, the tag recommendation task -suggesting suitable tags to users of such systems while they add resources to their collection -has been tackled. However, the (offline) evaluation of tag recommendation algorithms usually suffers from difficulties like the sparseness of the data or the cold start problem for new resources or users. Previous studies therefore often used so-called post-cores (specific subsets of the original datasets) for their experiments. In this paper, we conduct a large-scale experiment in which we analyze different tag recommendation algorithms on different cores of three real-world datasets. We show, that a recommender's performance depends on the particular core and explore correlations between performances on different cores.

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Abstract. This paper focuses on resource-aware and cost-effective indoor-localization at room-level using RFID technology. In addition to the tracking information of people wearing active RFID tags, we also include information about their proximity contacts. We present an evaluation using real-world data collected during a conference: We complement state-of-the-art machine learning approaches with strategies utilizing the proximity data in order to improve a core localization technique further.

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