Running Community Detection With Memgraph and Python NetworkX

Memgraph, an in-memory graph database, enhances the capabilities of the Python NetworkX package by providing permanent storage and additional graph analytics features, using a property graph model and the Cypher query language. This integration allows for more intuitive network exploration, overcoming the limitations of NetworkX's dependence on Python code alone. The blog post guides users on implementing a custom Cypher procedure within Memgraph to perform community detection and calculate betweenness centrality using NetworkX algorithms, such as the Girvan-Newman method. Users must create and mount a volume to access the query_modules directory for custom query creation, enabling procedures to be executed within Memgraph. The post also addresses the importance of transforming graphs to undirected types for accurate betweenness centrality calculations, illustrating the benefits of combining NetworkX with Memgraph for comprehensive network analysis.