GraphRoam is a Python library designed to efficiently generate random walks on large graphs using consumer-grade, RAM-limited hardware. With DuckDB and sparse matrix representations, GraphRoam ensures minimal memory usage while maintaining high performance.

• RAM Efficiency: Only the adjacency matrix is held in memory.
• Flexible Vertex Naming: Supports string or non-enumerated integer vertex names.
• Directed Graphs: Designed for directed, non-weighted graphs.
• Disk-Based Random Walks: Random walks are written to disk, not stored in memory.

To install GraphRoam, you can use pip:

pip install graphroam

To use GraphRoam, simply call the graphroam function with your parameters:

from graphroam import graphroam

graphroam(
    source_target_parquet='path/to/your/graph.parquet',
    walks_per_node=10,
    max_steps_per_node=50,
    mem_limit=16,  # Memory limit in GB
    workers=-1     # Number of parallel workers (-1 to use all available cores)
)

• source_target_parquet: Path to your Parquet file containing the graph edges.
• walks_per_node: Number of random walks per node. Default is 10.
• max_steps_per_node: Maximum steps per node in each random walk. Default is 50.
• mem_limit: Memory limit in GB for DuckDB. Setting this too low could result in high disk usage and longer processing times. Default is 16.
• workers: Number of parallel workers. Default is -1 (uses all available cores).

• The graph should be a directed, non-weighted graph.
• If you have an undirected graph, you can duplicate your data and flip the edges before feeding it into GraphRoam.
• The input file should be a Parquet file with the first column as the source node and the second column as the target node. Column headers are not important.

• Random walks are saved as multiple CSV files in a folder called random_walks.

• The size of the adjacency matrix held in RAM is highly dependent on your data, for reference; a 64 million node, 500M edge graph would occupy about 8GB of RAM.
• A decent SSD with enough space is needed to hold the temporary and final files.

1. Vertex Mapping: GraphRoam uses DuckDB to efficiently map vertex names to integers.
2. Sparse Matrix: A sparse matrix representation of the adjacency matrix is constructed and held in memory.
3. Random Walks: The library generates random walks and writes them to disk, ensuring minimal memory usage.

.
├── graphroam/
│   ├── __init__.py
│   ├── graphroam.py
├── tests/
├── requirements.txt
├── README.md
└── random_walks/
    ├── walk_1.csv
    ├── walk_2.csv
    └── ...

This project is licensed under the MIT License. See the LICENSE file for details.

Feel free to open issues or pull requests if you have suggestions or features to add. Join us in making GraphRoam even better!