Abstract
We present an efficient distributed graph database architecture for large scale social computing. The architecture consists of a distributed graph data processing system and a distributed graph data storage system. We leverage the advantages of both systems to achieve efficient social computing. We conduct extensive experiments to demonstrate the performance of our system. We employ four real-world, large scale social networks iV YouTube, Flicker, LiveJournal and Orkut as test data. We also implement several representative social applications and graph algorithms to examine the performance of our system. We employ two main optimization techniques in our system iVindexing and graph partitioning. Experimental results indicate that our system outperforms GoldenOrb, an implementation foreleg model from Google.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.