Bayan Algorithm Benchmark: 1,020 Randomly Generated Networks for Community Detection

1,020 randomly generated network files in .gml format from a study on the Bayan community detection algorithm. The collection includes 500 ABCD graphs, 500 LFR graphs, 10 Erdos-Renyi graphs, and 10 Barabasi-Albert graphs. It was created by Samin Aref and colleagues for research published in 2022 and 2023.

Use Cases

• Benchmarking community detection algorithms based on the described random graph models (ABCD, LFR, Erdos-Renyi, Barabasi-Albert).
• Studying algorithm performance on synthetic networks with known community structures.
• Evaluating the optimization of modularity, as referenced in the associated research articles.

Strengths

• Contains 1,020 distinct network files, providing a substantial testbed.
• Includes four distinct types of random graph models (500 ABCD, 500 LFR, 10 Erdos-Renyi, 10 Barabasi-Albert).
• Data is directly linked to published research on the Bayan algorithm, providing clear context.

Limitations

• Column-level documentation is absent; network structure and node/edge attributes must be inferred after download.
• Row count is unknown, which may limit suitability assessment for specific computational tasks.
• Last update date is unknown; freshness unverified.

Provenance

Source

Samin Aref, Hriday Chheda, and Mahdi Mostajabdaveh via paperswithcode and Figshare.

Collection Method

Randomly generated networks created for a study on community detection algorithms.