Degree Centrality

Algorithm introduction

Degree centrality measures node importance by counting incident edges. Nodes with higher degree are considered more influential, as they have more direct connections to other nodes in the graph.

Compute undirected degree centrality for all nodes and return them sorted by degree descending.

For more information, see Degree Centrality.

Data model

To implement the Degree Centrality algorithm in GQL, the following data model is used to create a graph type:

• Entities are represented by nodes of type Node, with properties id and rating.
• Relationships are represented by edges of type Link, with property weight.

For the complete definition of the graph type, see Prepare graph data.

Procedure implementation

To compute Degree Centrality results, a procedure is created.

For more information about the procedure, see Procedure implementation. The following section describes the key components of the procedure and how it works.

The procedure takes the following input parameters:

• This procedure does not require input parameters.

Additionally, the following variables are defined within the procedure:

Variable name	Variable type	Description
degree	Node-attached SumAgg aggregator value variable	Per-node incident edge count (undirected degree).
result_table	Table variable	Output table storing (vertex_id, degree) pairs.

The following steps outline the logic of the procedure:

1. Initialize per-node degree to 0.
2. For each undirected edge, increment degree for the source endpoint.
3. Export all (node ID, degree) pairs into result_table.
4. Return (vertex_id, degree), sorted by degree descending.

Steps

Run the following code blocks in order in the NebulaGraph Studio CONSOLE. For first-time setup, follow Prepare algorithm sample data to check where to run commands and how the directory is mounted.

1. Prepare graph data

1. Create a graph type that matches your data model.

   '''
   CREATE GRAPH TYPE IF NOT EXISTS degree_centrality_graph_type AS {
       NODE Node (LABEL Node {id INT PRIMARY KEY, rating INT}),
       EDGE Link (Node)-[LABEL Link {weight INT, MULTIEDGE KEY()}]->(Node)
   }
   '''
   

2. Download these CSV files and save them with their original names in examples/algorithms/degree_centrality/ under your deployment directory:

   • degree_centrality_node.csv: Node data.
   • degree_centrality_edge.csv: Edge data.
   Preview the CSV files

   degree_centrality_node.csv

   id,rating
   1,0
   2,2
   3,1
   4,0
   5,0
   6,2
   7,1
   8,2
   9,0
   10,1
   11,0
   12,2
   13,1
   14,1
   15,0
   16,2
   17,0
   18,1
   19,2
   20,0
   21,2
   22,1
   23,0
   24,1
   

   degree_centrality_edge.csv

   src_id,dst_id,weight
   1,2,4
   1,3,2
   1,4,7
   1,5,9
   2,3,1
   2,4,6
   2,5,5
   2,6,2
   3,4,3
   3,5,4
   3,6,8
   4,6,2
   4,7,6
   4,8,5
   5,6,4
   5,8,7
   5,9,6
   6,8,1
   6,9,5
   6,10,6
   7,9,2
   7,10,9
   8,9,3
   8,11,6
   8,12,4
   9,10,4
   9,12,7
   9,13,2
   10,12,1
   10,13,8
   10,14,4
   11,12,2
   11,14,5
   11,15,7
   12,14,3
   12,15,6
   12,16,5
   13,15,2
   13,16,7
   14,15,4
   14,17,8
   14,18,2
   15,16,1
   15,18,5
   15,19,3
   16,18,3
   16,19,6
   17,18,2
   17,20,7
   17,21,5
   18,19,4
   18,21,9
   18,22,6
   19,21,1
   19,22,5
   20,21,3
   20,23,6
   20,24,8
   21,22,2
   21,23,4
   21,24,7
   22,24,4
   23,24,1
   

3. Create a temporary graph based on the graph type and load the data files into the temporary graph.

   CREATE TEMPORARY GRAPH IF NOT EXISTS #degree_centrality_graph TYPED degree_centrality_graph_type
   

   '''
   USE #degree_centrality_graph IMPORT INTO GRAPH {
         NODE (v@Node {id: id, rating: rating}) FROM DATAFILE {
             PATH: "file:///examples/algorithms/degree_centrality/degree_centrality_node.csv",
             FORMAT: "CSV",
             DELIMITER: ','
         },
         EDGE (id: src_id)-[e@Link {weight: weight}]->(id: dst_id) FROM DATAFILE {
             PATH: "file:///examples/algorithms/degree_centrality/degree_centrality_edge.csv",
             FORMAT: "CSV",
             DELIMITER: ','
         }
     }
   '''
   

   After the import succeeds, run this query in the console to check the node count:

   '''
   USE #degree_centrality_graph
   MATCH (n)
   RETURN count(n) AS node_count
   '''
   

   The expected node_count is 24. If it differs, check the files and import results before continuing.

2. Create a procedure

Create a procedure to implement the Degree Centrality algorithm. For more information about the procedure, see Procedure implementation.

'''
CREATE OR REPLACE PROCEDURE degree_centrality()
RETURNS (vertex_id INT64, degree INT64)
COMMENT "Compute undirected degree centrality for all vertices and return them sorted by degree descending."
AS {
  NODE VALUE degree SumAgg<INT64> = 0

  TABLE result_table TYPED TABLE {vertex_id INT64, degree INT64}

  MATCH (s)-[e]-(t)
  PER PATH {
    SET s.@degree += 1
  }

  MATCH (s)
  PER NODE (s) {
    EXPORT s.id, s.@degree INTO result_table
  }

  FOR r IN result_table
  RETURN r.vertex_id AS vertex_id, r.degree AS degree
  ORDER BY degree DESC, vertex_id ASC
}
'''

3. Call your procedure

Compute the degree of each node.

USE #degree_centrality_graph CALL degree_centrality() RETURN vertex_id, degree

Returns:

Expand to view results

+-----------+--------+
| vertex_id | degree |
+-----------+--------+
| 6         | 7      |
| 9         | 7      |
| 12        | 7      |
| 15        | 7      |
| 18        | 7      |
| 21        | 7      |
| 4         | 6      |
| 5         | 6      |
| 8         | 6      |
| 10        | 6      |
| 14        | 6      |
| 2         | 5      |
| 3         | 5      |
| 16        | 5      |
| 19        | 5      |
| 1         | 4      |
| 11        | 4      |
| 13        | 4      |
| 17        | 4      |
| 20        | 4      |
| 22        | 4      |
| 24        | 4      |
| 7         | 3      |
| 23        | 3      |
+-----------+--------+

Save results to a file or graph

To save the returned results as CSV files or write them to a graph, see EXPORT. Match the exported fields to the returned columns and target properties.