Historical graphs (historical graphs/ evolving graphs) are graphs that aim to store all historical information in order to make later questions about its evolution. The different times in a historical graph are called snapshots. As the graph evolves, different snapshots have different topology making it more difficult to have a universal structure for efficient partitioning of the graph across different machines [1]. The approaches that have been applied so far involve temporal partitioning, topological partitioning or partitioning based on hash functions. Objectives: to investigate existing techniques for efficient partitioning of a historical graph and to compare them. Finding which applications are most efficient (node-centric, snapshot-based, time-centric systems).
Overview of techniques for partitioning historical graphs in distributed environments (only the theoretical part)
What is asked is to find the existing techniques for efficient partitioning of a historical graph to make a comparison and in which applications they are most efficient. The research piece will be done by me so I ask only bibliographic piece. I have additional papers available as a bibliography to be provided later.
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