Common Neighbors

Algorithm introduction

Common neighbors predicts potential links by counting shared neighbors between node pairs.

Return the common neighbors of two nodes identified by node property id and the total count.

For more information, see Common Neighbors.

Data model

To implement the Common Neighbors 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 Common Neighbors 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:

• left_id: INT64. ID of the first node.
• right_id: INT64. ID of the second node.

Additionally, the following variables are defined within the procedure:

Variable name	Variable type	Description
result_neighbors	Global SumAgg aggregator value variable	List of common neighbor node IDs for the queried pair.
result_count	Global SumAgg aggregator value variable	Count of common neighbors.
seen_left	Node-attached SumAgg aggregator value variable	Per-node flag: node is adjacent to the left query node.
seen_right	Node-attached SumAgg aggregator value variable	Per-node flag: node is adjacent to the right query node.

The following steps outline the logic of the procedure:

1. For the left node (left_id), traverse all its edges and mark each neighbor with seen_left.
2. For the right node (right_id), traverse all its edges and mark each neighbor with seen_right.
3. Collect all nodes with both seen_left and seen_right (excluding left_id and right_id themselves) into result_neighbors; count them in result_count.
4. Return the list of common neighbor IDs and the total count.

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 common_neighbors_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/common_neighbors/ under your deployment directory:

   • common_neighbors_node.csv: Node data.
   • common_neighbors_edge.csv: Edge data.
   Preview the CSV files

   common_neighbors_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
   

   common_neighbors_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 #common_neighbors_graph TYPED common_neighbors_graph_type
   

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

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

   '''
   USE #common_neighbors_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 Common Neighbors algorithm. For more information about the procedure, see Procedure implementation.

'''
CREATE OR REPLACE PROCEDURE common_neighbors(left_id INT64, right_id INT64)
RETURNS (neighbor_ids LIST<INT64>, common_neighbor_count INT64)
COMMENT "Return the common neighbors of two vertices identified by vertex property id and the total count."
AS {
  VALUE result_neighbors ListAgg<INT64>
  VALUE result_count SumAgg<INT64> = 0

  NODE VALUE seen_left OrAgg = false
  NODE VALUE seen_right OrAgg = false

  MATCH (s)-[e]-(n)
  WHERE s.id = left_id
  PER PATH {
    SET n.@seen_left += true
  }

  MATCH (s)-[e]-(n)
  WHERE s.id = right_id
  PER PATH {
    SET n.@seen_right += true
  }

  MATCH (n)
  WHERE n.@seen_left = true AND n.@seen_right = true AND n.id <> left_id AND n.id <> right_id
  PER NODE (n) {
    SET @result_neighbors += n.id
    SET @result_count += 1
  }

  RETURN @result_neighbors AS neighbor_ids, @result_count AS common_neighbor_count
}
'''

3. Call your procedure

Find the common neighbors of nodes 6 and 9, returning their IDs and count.

USE #common_neighbors_graph CALL common_neighbors(6, 9) RETURN neighbor_ids, common_neighbor_count

Returns:

+--------------+-----------------------+
| neighbor_ids | common_neighbor_count |
+--------------+-----------------------+
| [5, 10, 8]   | 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.