Leveraging Neo4j for Data Science: Evaluating Traversal Efficiency in GDS and APOC for Directed Acyclic Graphs

This paper presents a benchmark study of Breadth-First Search (BFS) and Depth-First Search (DFS) traversal algorithms applied to complex Directed Acyclic Graphs (DAGs) within Neo4j, utilizing the Graph Data Science (GDS) and Awesome Procedures on Cypher (APOC) libraries. DAGs are widely used in fields like data science, project management, software engineering, and bioinformatics to manage dependencies without cycles. Our experiments evaluate the performance of GDS and APOC on DAGs generated from Feature Models representing dependencies in Software Product Lines (SPL). Results indicate that GDS consistently outperforms APOC, particularly for large and intricate graph structures. These findings highlight the importance of optimized traversal techniques for managing complex DAGs efficiently, offering insights into scalability and performance improvements for real-world applications.