A social network consists of people who interact in some way such as members of online communities sharing information via the WWW. To learn more about how to facilitate community building e.g. in organizations, it is important to analyze the interaction behavior of their members over time. So far, many tools have been provided that allow for the analysis of static networks and some for the temporal analysis of networks -however only on the vertex and edge level. In this paper we propose two approaches to analyze the evolution of two different types of online communities on the level of subgroups: The first method consists of statistical analyses and visualizations that allow for an interactive analysis of subgroup evolutions in communities that exhibit a rather membership structure. The second method is designed for the detection of communities in an environment with highly fluctuating members. For both methods, we discuss results of experiments with real data from an online student community.

In this paper we present an application of our incremental graph clustering algorithm (DENGRAPH) on a data set obtained from the music community site Last.fm. The aim of our study is to determine the music preferences of people and to observe how the taste in music changes over time. Over a period of 130 weeks, we extract for each interval user profiles of 1,800 users that represent their music listening behavior. By building and incrementally clustering a graph of similar users, we obtain groups of people with similar music preferences. We label these clusters with genres according to the user profiles of the cluster members. Due to the incremental nature of DENGRAPH we show how clusters evolve over time. Besides the growth and decrease of clusters we observe how new clusters emerge and old clusters die. Furthermore, we show the merge and split of clusters. The results of our experiments indicate that DEN-GRAPH is particularly useful to efficiently detect groups of similar users and to track them over time.

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DenGraph‐HO is an extension of the density‐based graph clustering algorithm DenGraph. It is able to detect dense groups of nodes in a given graph and produces a hierarchy of clusters, which can be efficiently computed. The generated hierarchy can be used to investigate the structure and the characteristics of social networks. Each hierarchy level provides a different level of detail and can be used as the basis for interactive visual social network analysis. After a short introduction of the original DenGraph algorithm, we present DenGraph‐HO and its top‐down and bottom‐up approaches. We describe the data structures and memory requirements and analyse the run‐time complexity. Finally, we apply the DenGraph‐HO algorithm to the real‐world datasets obtained from the online music platform Last.fm and from the former US company Enron.

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