Postmortem Computation of Pagerank on Temporal Graphs

Temporal graphs capture changes in relational data over time and have been of increasing interest to data analysts. Most research focuses on streaming algorithms that incrementally update an analysis to account for the changes in the graph. However, one can also be interested in understanding the nature of changes in the graph over time. In such a case, they perform a postmortem analysis on different points in time where all the data known in advance We study in this paper a postmortem analysis of Pagerank over-time on graphs that are defined by temporal relational event databases. A relation between two entities at a particular point in time will form an edge between these two entities and that will remain in the graph for a fixed period of time. While one can reuse a streaming algorithm for that purpose, leveraging the availability of all the data from the beginning can be beneficial. Postmortem analysis enables encoding the temporal graph with a more efficient graph representation. Also, it provides an additional level of parallelism since one can not only parallelize within a particular timestamp but also across different timestamps. We will show that depending on the properties of the temporal data, either parallelization can be better, and in some cases, a combination of both approaches is preferable. We experimentally show across 7 databases and across different temporal derivations of the graph that postmortem analysis can be between 50 times and 880 times faster than streaming analysis.