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An Evaluation Framework for Groups' Clustering Algorithms in Social Networks - The Use Case of a Meetup Dataset of Older Adults

Older adults are increasingly using Social Networks Sites to support their social interactions. Moreover, the popularity of such network sites, the availability of datasets and recent progress in the computing systems and machine learning areas have made social network analysis a current important area of research. In this context, our current research aim is to investigate how these technological platforms are affecting the lifestyle of older adults. In this paper, we propose a Java oriented framework that can assist researchers in this area in the analysis of social networks groups, specifically in the comparison of user’s groups creation from existing cluster-based algorithms. To validate the proposed framework we use a dataset extracted from the Meetup social network a website providing members software services to schedule events using a common platform. For our study, we filtered the data for the specific group of older adults. The framework proposes several ways of evaluating the quality of the data, and is extensible to other clustering algorithms and evaluation metrics. Currently, we have tested our framework with the following well known clustering algorithms: k-means, fuzzy k-means, and affinity propagation. We report some preliminary results obtained by using the proposed framework and the above clustering algorithms using the extracted Meetup dataset.