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Efficient and Scalable Aggregate Computation on Temporal Graphs

Many applications such as social networks generate big volumes of graph data that has additional temporal information or changes rapidly, which has lead to a significant amount of research on temporal graphs. Existing and ongoing work on temporal graphs has focused on path problems and graph databases in general. Aggregations, which are very common for relational data, are just as insightful for temporal graph data, but need to be computed efficiently and scalable. To aggreagte efficiently, useful operations on graphs need to be selected, the composition of aggregation functions needs to be investigated, and the distribution of the calculation must be studied. After tackling the research question with the simplifying assumption of static communities (which are highly coupled nodes), then the parallelization of the problem is investigated, and finally, the simplifying assumption is removed for the general case of dynamic communities. As aggregations play an important role in (temporal) relational data, this direction of research might establish them in temporal graphs just as well.