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In this study, we present the first results of a complete implementation ofthe LDBC SNB benchmark -- interactive short, interactive complex, and businessintelligence -- in two native graph database systems---Neo4j and TigerGraph. Inaddition to thoroughly evaluating the performance of all of the 46 queries inthe benchmark on four scale factors -- SF-1, SF-10, SF-100, and SF-1000 -- andthree computing architectures -- on premise and in the cloud -- we also measurethe bulk loading time and storage size. Our results show that TigerGraph isconsistently outperforming Neo4j on the majority of the queries---by two ormore orders of magnitude (100X factor) on certain interactive complex andbusiness intelligence queries. The gap increases with the size of the datasince only TigerGraph is able to scale to SF-1000---Neo4j finishes only 12 ofthe 25 business intelligence queries in reasonable time. Nonetheless, Neo4j isgenerally faster at bulk loading graph data up to SF-100. A key to our study isthe active involvement of the vendors in the tuning of their platforms. Inorder to encourage reproducibility, we make all the code, scripts, andconfiguration parameters publicly available online.

In-Depth Benchmarking of Graph Database Systems with the Linked Data Benchmark Council (LDBC) Social Network Benchmark (SNB)