Distributed Social Network Analysis[go to overview]
Graphs of social networks have reached sizes of billions of edges. The scale of these graphs poses challenges in their analysis. Most graph algorithms are data driven and memory-based approaches usually do not scale because of the limit in capacity of single machines. In order to analyze the connectivity and the clustering coefficient of a huge graph, we need distributed and parallel approaches to handle the amount of data of these graphs. The goal of our research project was to find a framework that is able to handle the calculation of different graph properties.
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