# Experimental Evaluation of Graph Databases: JanusGraph, Nebula Graph, Neo4j, and TigerGraph
## 2 Related Work
The performance of the database was analyzed using the LDBC SNB benchmark, using only short queries and the insert and update queries, leaving out the complex queries. It is not possible to conclude whether Graph Nebula is a graph database with excellent performance because it was not compared with other databases. In Ref. [7], [PERSON] et al. compare the graph databases Neo4j, JanusGraph, and Dgraph using a version control system called Git. The study analyzed read/write operations and the performance of tasks such as inserting new data into graphs. The authors concluded that Neo4j outperformed the other graph databases, offering better read and write performance and the ability to handle high data loads. [PERSON] et al. in Ref. [8] analyze the evolution of Neo4j and TigerGraph databases compared to relational databases and show that it is possible to adopt graph databases in several areas, especially in biomedicine, where they have a more intuitive data visualization and representation for the user. The study concludes that graph databases offer better scalability options, more accessible query languages, and a more intuitive data representation. [PERSON] et al. in Ref. [9] compare multi-model and single-model databases because the multi-model combines the most advantageous features of different databases. Although a multi-model database can be versatile, it runs the risk of performing worse than single-model databases. The study compares the multi-model database OrientDB with Neo4j, a graph database, and with MongoDB, which is document-oriented. The comparison is based on the performance of the databases when performing various queries focused on multiple properties. The queries were divided into two distinct categories, wherein the first was graph queries, and the second was document-type data. For graph queries, OrientDB outperformed Neo4j at greater depths when finding the shortest path between nodes with different properties. When it came to traversing other nodes of depth-three, though, Neo4j proved the best. The document type data queries focused on the difference between an indexed and an unindexed query and concluded that MongoDB has a lower query execution time, as compared to OrientDB. [PERSON] et al. in Ref. [10] used regular graph and hypergraph models in graph databases. The performance of the Neo4j graph database was evaluated using 12 queries for each model, and the queries involved hyperedges and binary edges. The study concludes that the hyper-graph model uses more storage because additional nodes are required for this model, resulting in more associated benefits. However, the execution time is significantly reduced for queries involving multiple node types. [PERSON] et al. in Ref. [11] state that graph databases outperform relational databases when querying data. The authors note that while the back-end provides secure transactions and storage, native graph databases attract user attention by promising better performance than that of traditional databases. The study analyzes a set of analytical queries in Neo4j and two leading commercial relational databases, the identities of which were withheld from publication due to licensing restrictions. In addition to these queries, pattern-matching queries were also used, including paths that filter specific node labels without any edge-type restrictions. They also used methods to filter an edge-type without node label constraints,including ways to filter both node and edge labels and retain paths that contain cycles. The execution times of cycle queries in Neo4j were compared with the execution times of SQL queries in the relational database. The study concludes that relational databases outperform Neo4j for analytical queries, but Neo4j is faster for queries that do not filter specific edges. [PERSON] et al. [12] present a detailed study of the main features of the graph database TigerGraph.