Graph data partition models for online social networks

Online social networks have become important vehicles for connecting people for work and leisure. As these networks grow, data that are stored over these networks also grow, and management of these data becomes a challenge. Graph data models are a natural fit for representing online social networks but need to support distribution to allow the associated graph databases to scale while offering acceptable performance. We provide scalability by considering methods for partitioning graph databases and implement one within the Neo4j architecture based on distributing the vertices of the graph. We evaluate its performance in several simple scenarios and demonstrate that it is possible to partition a graph database without incurring significant overhead other than that required by network delays. We identify and discuss several methods to reduce the observed network delays in our prototype.