An investigation of big graph partitioning methods for distribution of graphs in vertex-centric systems

Abstract

Relations among data entities in most big data sets can be modeled by a big graph. Implementation and execution of algorithms related to the structure of big graphs is very important in different fields. Because of the inherently high volume of big graphs, their calculations should be performed in a distributed manner. Some distributed systems based on vertex-centric model have been introduced for big graph calculations in recent years. The performance of these systems in terms of run time depends on the partitioning and distribution of the graph. Therefore, the graph partitioning is a major concern in this field. This paper concentrates on big graph partitioning approaches for distribution of graphs in vertex-centric systems. This briefly discusses vertex-centric systems and formulates different models of graph partitioning problem. Then, a review of recent methods of big graph partitioning for these systems is shown. Most recent methods of big graph partitioning for vertex centric systems can be categorized into three classes: (i) stream-based methods that see vertices or edges of the graph in a stream and partition them, (ii) distributed methods that partition vertices or edges in a distributed manner, and (iii) dynamic methods that change partitions during the execution of algorithms to obtain better performance. This study compares the properties of different approaches in each class and briefly reviews methods that are not in these categories. This comparison indicates that The streaming methods are good choices for initial load of the graph in Vertex-centric systems. The distributed and dynamic methods are appropriate for long-running applications.