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grph centrality

Calculate centrality metrics for nodes.

Synopsis​

grph centrality <file> [--type degree|betweenness|closeness|pagerank|eigenvector] [--top N] [--json]

Description​

The centrality command calculates various centrality metrics to identify the most important or influential nodes in the graph. Different centrality measures capture different aspects of node importance.

Arguments​

ArgumentDescription
filePath to the GEXF file (required)

Options​

OptionDefaultDescription
--typedegreeType of centrality: degree, betweenness, closeness, pagerank, eigenvector
--top10Number of top nodes to display
--jsonOutput as JSON (includes all nodes)
--helpShow help message

Centrality Types​

Degree Centrality​

The simplest measure: how many connections a node has. Nodes with high degree centrality are "hubs" with many direct connections.

grph centrality network.gexf --type degree

Betweenness Centrality​

Measures how often a node lies on the shortest path between other nodes. High betweenness nodes are "bridges" that control information flow.

grph centrality network.gexf --type betweenness

Closeness Centrality​

Measures how close a node is to all other nodes. High closeness nodes can quickly reach or spread information to the entire network.

grph centrality network.gexf --type closeness

PageRank​

Google's famous algorithm. Nodes are important if they are linked to by other important nodes. Good for finding authoritative nodes.

grph centrality network.gexf --type pagerank

Eigenvector Centrality​

Similar to PageRank but for undirected graphs. A node is important if it's connected to other important nodes.

grph centrality network.gexf --type eigenvector

Examples​

Basic Usage​

grph centrality network.gexf

Output:

     Degree Centrality (Top 10)
+------+----------+----------+
| Rank | Node     | Score    |
+------+----------+----------+
| 1    | hub1     | 0.450000 |
| 2    | server1  | 0.350000 |
| 3    | server2  | 0.300000 |
+------+----------+----------+

PageRank Analysis​

grph centrality dagger-graph.gexf --type pagerank --top 20

JSON Output​

grph centrality network.gexf --type betweenness --json
{
  "type": "betweenness",
  "scores": {
    "hub1": 0.523456,
    "server1": 0.234567,
    "..."
  }
}

Use Cases​

Find Critical Dependencies​

Use betweenness centrality to find components that many others depend on:

grph centrality dagger-graph.gexf --type betweenness --top 5

Identify Hub Components​

Use degree centrality to find highly connected components:

grph centrality dagger-graph.gexf --type degree

Find Authoritative Sources​

Use PageRank to find the most referenced nodes:

grph centrality citation-graph.gexf --type pagerank