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46 result in a better community. Bagrow [13] evaluated the performance of the various algorithms (and one additional newly proposed one), and found that the algorithm of Leo et al. performed the best on synthetically generated graphs. It is important to note that none of these algorithms has been validated on a large-scale social network, primarily due to the lack of data availability. Typically, the algorithms are evaluated on synthetically generated graphs, product recommendation networks, or very small social networks such as Zachary’s karate club [171], consisting of 34 members. Thus, it is not known whether they can detect communities in online social networks. In this thesis, we demonstrate the limitations of these approaches on data taken from an online social network, and present a new algorithm that addresses these limitations. 3.3.4 Observations of communities A few studies have examined the community structures that exist in online social networks. The most notable of these is the work by Nazir et al. [113], which presented a large-scale measurement study of usage characteristics of applications in Facebook. They launched a few Facebook applications and, using the data collected from these applications, they characterized the workload, the structure of user interactions, and the existence of communities in the network. Their results, however, do not paint a complete picture of the Facebook network as they are only able to collect data on users who installed one of their applications. In contrast, we are able to collect dataPDF Image | Online Social Networks: Measurement, Analysis, and Applications to Distributed Information Systems
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