Understanding Graph Databases: A Comprehensive Tutorial and Survey

TL;DR

This comprehensive tutorial and survey explains fundamental graph theory concepts, practical applications, and leading graph database systems, emphasizing their advantages over traditional databases and providing hands-on guidance.

Contribution

It offers an extensive overview of graph databases, including algorithms, system comparisons, and practical tutorials, filling a gap in accessible, detailed educational resources.

Findings

Graph databases efficiently manage complex interconnected data.

Leading systems like Neo4j and Amazon Neptune offer unique features.

Community detection algorithms facilitate large network clustering.

Abstract

This tutorial serves as a comprehensive guide for understanding graph databases, focusing on the fundamentals of graph theory while showcasing practical applications across various fields. It starts by introducing foundational concepts and delves into the structure of graphs through nodes and edges, covering different types such as undirected, directed, weighted, and unweighted graphs. Key graph properties, terminologies, and essential algorithms for network analysis are outlined, including Dijkstras shortest path algorithm and methods for calculating node centrality and graph connectivity. The tutorial highlights the advantages of graph databases over traditional relational databases, particularly in efficiently managing complex, interconnected data. It examines leading graph database systems such as Neo4j, Amazon Neptune, and ArangoDB, emphasizing their unique features for handling large datasets. Practical instructions on graph operations using NetworkX and Neo4j are provided, covering node and edge creation, attribute assignment, and advanced queries with Cypher. Additionally, the tutorial explores common graph visualization techniques using tools like Plotly and Neo4j Bloom, which enhance the interpretation and usability of graph data. It also delves into community detection algorithms, including the Louvain method, which facilitates clustering in large networks. Finally, the paper concludes with recommendations for researchers interested in exploring the vast potential of graph technologies.