Large-scale temporal graphs can serve as a model in many application scenarios. Owing to the popularity of online social networks and increased research interest in gathering and analysing data about human behaviour and interaction reality mining temporal graphs gain attraction in social network analysis and more specifically in the analysis of dynamic processes in social networks. Originally developed for web-graph analysis, systems supporting a Pregel-style processing paradigm can be used to process large-scale social network data. However, current methods for social network analysis require data to be processed off-line, or lack support for temporal graphs, or support datasets of limited size only. In this work we present a cloud-based distributed processing framework extending the Pregel paradigm for large-scale temporal graphs. By using computing resources in the cloud this system is scalable and prepared to scale for the massive datasets that occur in social network analysis.
['Matthias Steinbauer', 'Gabriele Anderst-Kotsis']
DynamoGraph: extending the Pregel paradigm for large-scale temporal graph processing