Updated on 2024-01-03 GMT+08:00

PersonalRank

Overview

PersonalRank is also called Personalized PageRank. It inherits the idea of the classic PageRank algorithm and uses the graph link structure to recursively calculate the importance of each node. However, unlike the PageRank algorithm, to ensure that the access probability of each node in the random walk can reflect user preferences, the PersonalRank algorithm returns each hop to the source node at a (1-alpha) probability during random walk. Therefore, the relevance and importance of network nodes can be calculated based on the source node. (The higher the PersonalRank value, the higher the correlation/importance of the source node.)

Application Scenarios

This algorithm applies to fields such as product, friend, and web page recommendations.

Parameter Description

Table 1 PersonalRank algorithm parameters

Parameter

Mandatory

Description

Type

Value Range

Default Value

source

Yes

Node ID

String

-

-

alpha

No

Weight coefficient

Double

A real number between 0 and 1 (excluding 0 and 1)

0.85

convergence

No

Convergence

Double

A real number between 0 and 1 (excluding 0 and 1)

0.00001

max_iterations

No

Maximum iterations

Int

1-2,000

1000

directed

No

Whether an edge is directed

Bool

true or false

true

  • alpha determines the jump probability coefficient, also called damping coefficient, which is a computing control variable in the algorithm.
  • convergence defines the sum and upper limit of absolute values of each vertex in each iteration compared with the last iteration. If the sum is less than the value, the computing is considered to be converged and the algorithm stops.

Precautions

When the convergence is set to a large value, the iteration will stop quickly.

Example

Select the algorithm in the algorithm area of the graph engine editor. For details, see Analyzing Graphs Using Algorithms.

Set source to Lee, alpha to 0.85, convergence to 0.00001, max_iterations to 1000, and directed to true. The sub-graph formed by top nodes in the calculation result is displayed on the canvas. The size of a node varies with the PersonalRank values. The JSON result is displayed in the query result area.