Carina Saal, Pavithran Sakamuri, Tung-Yin Kuo, Roman Sokolov

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.

15.10.15 - 10:15
B 016