Centrality Algorithms in Neo4j

1. Introduction

Centrality algorithms are essential for identifying the most important nodes in a graph. In Neo4j, these algorithms play a crucial role in understanding the structure and dynamics of networks.

2. Types of Centrality

• Degree Centrality
• Closeness Centrality
• Betweenness Centrality
• Eigenvector Centrality

3. Algorithm Overview

Each centrality type has its own method for calculating importance:

1. Degree Centrality: Measures the number of connections a node has.
2. Closeness Centrality: Measures how quickly a node can access other nodes.
3. Betweenness Centrality: Measures the number of times a node acts as a bridge along the shortest path between two other nodes.
4. Eigenvector Centrality: Measures a node's influence based on its connections to other influential nodes.

4. Code Examples

            // Degree Centrality
            MATCH (n)
            RETURN n.name AS Node, size((n)--()) AS Degree
            ORDER BY Degree DESC
            LIMIT 10
            

            // Betweenness Centrality
            CALL algo.betweenness.stream('Person', 'FRIEND', {direction: 'BOTH'})
            YIELD nodeId, score
            RETURN algo.get.node.byId(nodeId).name AS Name, score
            ORDER BY score DESC
            LIMIT 10
            

5. Best Practices

• Use the correct algorithm for the type of analysis.
• Optimize queries with appropriate indexing.
• Consider graph size and complexity when selecting algorithms.

6. FAQ