MYKHAILENKO , Hlib

In distributed graph computation, graph partitioning is an important preliminary step because the computation time can significantly depend on how the graph has been split among the different executors. In this thesis we explore the graph partitioning problem. Recently, edge partitioning approach has been advocated as a better approach to...

In this paper we focus on vertex-cut graph partitioning and we investigate how it is possible to evaluate the quality of a partition before running the computation. To this purpose we scrutinize a set of metrics proposed in literature. We carry experiments with the widely-used framework for graph processing Apache GraphX and we perform an...

Deploying graph on a cluster requires its partitioning into a number of subgraphs, and assigning them to different machines. Two partitioning approaches have been proposed: vertex partitioning and edge partitioning. In the edge partitioning approach edges are allocated to partitions. Recent studies show that, for power-law graphs, edge...

In distributed graph computation, graph partitioning is an important preliminary step, because the computation time can significantly depend on how the graph has been split among the different executors. In this paper, we propose a framework for distributed edge partitioning based on simulated annealing. The framework can be used to optimize a...

In distributed graph computation, graph partitioning is an important preliminarystep, because the computation time can significantly depend on how the graph has been split amongthe different executors. In this paper, we propose a framework for distributed edge partitioningbased on simulated annealing. The framework can be used to optimize a...