Abstract

Graph Partitioning is one of the favorite research topics among researchers since the 70s. It attracts a diverse group of researchers from various fields such as engineering, science and mathematics. In the last decade, the graphs have increased in size to billions of vertices. Despite the fact that storage devices have become cheaper, processing these huge spanning graphs is not possible for a single machine. This call for the need of partitioning the graph so a group of machines can perform various parallel calculations on them which would save time and produce quick results. The research problem is that the ratio of boundary vertices to interior vertices increases with the increase in number of parti- tions for existing partitioning techniques available. To address this issue, the random edge selection method of Graph Lab algorithm was replaced with four suggested edge sorting techniques. The results were compared with the random edge selection method of Graph Lab using various performance parameters.

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