flatG

flatG is an open-source graph-processing toolkit designed to handle large graphs efficiently by using a flat-memory layout. It aims to provide a lightweight, language-friendly interface for building and analyzing graphs on a single machine. The project offers core libraries in C++ with bindings for Python and JavaScript, allowing use in data science workflows and web applications.

Data model and storage: Graphs are stored in flat arrays that resemble a compressed sparse row representation. Core structures include an index array that marks the start of each node's adjacency, and a single edge target array. Optional weights produce a CSR-like graph; supports directed and undirected graphs. Large graphs can be memory-mapped to disk to reduce RAM usage.

Algorithms and API: flatG provides common graph algorithms, including breadth-first search, depth-first search, single-source shortest paths (Dijkstra, Bellman-Ford), PageRank, connected components, and k-core decomposition. The API emphasizes simple construction via add_node, add_edge, and iteration helpers, as well as streaming I/O to load graphs from edge lists or CSV.

Development and history: flatG originated in a community-driven project begun in the late 2010s with the aim of enabling high-performance graph analytics on commodity hardware. The project is maintained in public repositories with broad contributor participation. Documentation covers installation, data models, algorithms, and example analyses.

Applications and limitations: It is well-suited for single-node analytics, prototyping, education, and small-to-medium datasets. It is not a distributed graph database and does not provide built-in sharding or multi-machine transaction support. Performance depends on cache locality and memory availability; for very large graphs, out-of-core techniques and careful graph representation are recommended.