Key Takeaways: Distributed graph analytics faces challenges in partitioning large graphs due to limited memory on single nodes. Researchers have proposed new partitioning methods that reduce edge cuts, use hierarchical clustering, or employ streaming graph partitioning techniques. Coarsening graphs allows for more efficient partitioning and reducing the complexity of finding trades. Sub-partitioning divides each coarsened vertex into smaller sub-partitions to ensure efficient graph processing while avoiding overlapping sub-partitions.
In summary, this article provides an overview of recent advances in graph partitioning techniques for distributed graph analytics. By prioritizing edge cuts, using hierarchical clustering, or employing streaming graph partitioning, researchers have developed new methods to efficiently partition large graphs. Coarsening graphs and sub-partitioning further improve the efficiency of distributed graph processing by reducing computational complexity and maintaining data locality.
Refinement-Based Graph Partitioning: A Survey
