Graph Algorithms¶

Grafeo includes a library of built-in graph algorithms for common analysis tasks.

Coming Soon

The algorithms module is under active development. This page documents the planned API and available algorithms.

Algorithm Categories¶

Path Finding

Shortest paths, all paths, and path analysis.

Path Algorithms
Centrality

PageRank, betweenness, closeness, and degree centrality.

Centrality
Community Detection

Louvain, label propagation, and connected components.

Community Detection
Link Prediction

Predict missing or future relationships.

Link Prediction
Similarity

Node similarity and graph matching.

Similarity
Graph Metrics

Density, diameter, clustering coefficient.

Graph Metrics

Quick Reference¶

Algorithm	Category	Complexity	Use Case
Shortest Path	Path	O(V + E)	Navigation, routing
PageRank	Centrality	O(V + E)	Ranking, importance
Louvain	Community	O(V log V)	Clustering
Connected Components	Community	O(V + E)	Graph structure
Triangle Count	Metrics	O(E^1.5)	Clustering analysis

Using Algorithms¶

From GQL¶

-- PageRank
CALL grafeo.pagerank({
    node_label: 'Page',
    relationship_type: 'LINKS',
    damping: 0.85,
    iterations: 20
})
YIELD nodeId, score
MATCH (p:Page) WHERE id(p) = nodeId
RETURN p.url, score
ORDER BY score DESC
LIMIT 10

From Python¶

import grafeo
from grafeo.algorithms import pagerank, shortest_path

db = grafeo.Database()

# Run PageRank
scores = pagerank(db, damping=0.85, iterations=20)
for node_id, score in scores.items():
    print(f"Node {node_id}: {score:.4f}")

# Find shortest path
path = shortest_path(db, source_id=1, target_id=100)
print(f"Path length: {len(path)}")

From Rust¶

use grafeo::algorithms::{pagerank, PageRankConfig};

let config = PageRankConfig {
    damping: 0.85,
    iterations: 20,
    tolerance: 1e-6,
};

let scores = pagerank(&db, config)?;

for (node_id, score) in scores.iter() {
    println!("Node {}: {:.4}", node_id, score);
}

Algorithm Configuration¶

Most algorithms accept configuration parameters:

# PageRank configuration
pagerank(db,
    damping=0.85,           # Damping factor (0-1)
    iterations=20,          # Max iterations
    tolerance=1e-6,         # Convergence threshold
    node_label='Page',      # Filter by label
    relationship_type='LINKS'  # Filter by type
)

Custom Algorithms¶

See Extending Grafeo to learn how to add custom algorithms.

Performance Considerations¶

Graph Size - Large graphs may require more memory
Iterations - More iterations = better accuracy, longer runtime
Parallelism - Many algorithms support parallel execution
Caching - Results can be cached for repeated queries