Abstract. In this paper we present the novel SD-Map algorithm for exhaustive but efficient subgroup discovery. SD-Map guarantees to identify all interesting subgroup patterns contained in a data set, in contrast to heuristic or samplingbased methods. The SD-Map algorithm utilizes the well-known FP-growth method for mining association rules with adaptations for the subgroup discovery task. We show how SD-Map can handle missing values, and provide an experimental evaluation of the performance of the algorithm using synthetic data.

Subgroup discovery is a broadly applicable descriptive data mining technique for identifying interesting subgroups according to some property of interest. This article summarizes fundamentals of subgroup discovery, before that it also reviews algorithms and further advanced methodological issues. In addition, we briefly discuss tools and applications of subgroup discovery approaches. In that context, we also discuss experiences and lessons learned and outline some of the future directions in order to show the advantages and benefits of subgroup discovery.

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.

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Abstract-While the analysis of online social networks is a prominent research topic, offline real-world networks are still not covered extensively. However, their analysis can provide important insights into human behavior. In this paper, we analyze influence factors for link prediction in human contact networks. Specifically, we consider the prediction of new links, and extend it to the analysis of recurring links. Furthermore, we consider the impact of stronger ties for the prediction. The results and insights of the analysis are a first step onto predictability applications for human contact networks. I. INTRODUCTIONWith the growing amount of social data, ubiquitous systems, and mobile social media applications transcending everyday life, the analysis of social networks is receiving increased attention. This especially relates to the dynamics and the creation of links between the networks' subjects [20], important aspects of offline social networking still remains largely unexplored. The analysis of such networks can potentially provide more direct answers to fundamental questions, e. g., how do personal links get established, is it possible to correlate this with roles, how does the intensity of personal communication evolve?In this paper, we aim at providing first insights for answering such questions. We focus on real-world offline networks of human contacts, that is, face-to-face conversations between persons. In contrast to virtual networks, these contacts were acquired using a ubiquitous RFID-based system that allows us to collect face-to-face contacts. Thus, we can observe and analyze social interaction at a very detailed level, including the specific event sequences and durations.We [16] in the context of networks of human contacts. We aim to predict new contacts based on network properties, as an adaptation of methods for online social networks. In addition, we extend the analysis in two important directions: First, we consider the length of the contacts in more detail, and analyze the impact of longer conversations. Second, we consider the prediction of future recurring contacts, i. e., renewed contacts between specific actors. For these, we analyze influence factors and patterns for establishing such contacts, and also consider their specific durations in a fine-grained dynamic analysis. Essentially, this leads to the analysis of the impact of stronger ties for new and recurring contacts.

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