Graph Benchmarks¶
Measures graph algorithm performance across traversal, centrality, community detection, and Node2Vec embedding generation. Benchmarks compare muninn's built-in TVFs against alternative SQLite graph engines where available.
Engines¶
| Engine | Library | Traversal | Centrality | Community | Node2Vec |
|---|---|---|---|---|---|
| muninn | muninn | BFS, DFS, shortest path, components, PageRank | degree, betweenness, closeness | Leiden | Yes |
| graphqlite | GraphQLite | BFS, DFS, shortest path, components, PageRank | — | Leiden | — |
Graph Models¶
| Model | Description | Key Property |
|---|---|---|
| Erdos-Renyi | Random edges, uniform probability | Uniform degree distribution |
| Barabasi-Albert | Preferential attachment | Power-law (scale-free) degree distribution |
Benchmark Dimensions¶
Graphs are tested at multiple sizes (100 to 10,000 nodes) and average degrees (3 to 20 edges per node), producing a range from sparse to dense topologies.
Traversal¶
BFS, DFS, shortest path, components, and PageRank. Both engines.
Centrality¶
Degree, betweenness (Brandes algorithm), and closeness centrality. Muninn-only — measures how centrality computation scales with graph density.
Community Detection¶
Leiden algorithm for community detection. Compared across both engines.
Node2Vec¶
Random walk generation and Skip-gram training with Negative Sampling. Tests vary walk parameters (p, q) and embedding dimensionality (64, 128).
Traversal¶
BFS¶
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DFS¶
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Shortest Path¶
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Connected Components¶
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PageRank¶
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3756.147,
299032.738
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100,
500,
1000,
5000,
10000,
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Centrality¶
Degree Centrality¶
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Betweenness Centrality¶
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Closeness Centrality¶
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Community Detection¶
Leiden Community Detection¶
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Insertion Throughput¶
Insertion Throughput¶
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