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PageRank (1.0.0)

**Table 1** Parameter description
Parameter	Mandatory	Description	Type	Value Range	Default Value
alpha	No	Weight coefficient (also called damping 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	Integer	1 to 2000	1000
directed	No	Whether to consider the edge direction	Boolean	true or false	true

Iterations and convergence
- The algorithm is terminated when either of the following conditions is met:
  The maximum number of iterations is reached.
  
  The convergence precision is met.
- Generally, a smaller convergence precision and larger number of iterations lead to a higher efficiency of the algorithm.
- To meet a certain convergence precision, you should set the number of iterations as large as possible.
- A larger number of iterations means a longer algorithm running time. To ensure that the algorithm runs at a certain number of iterations (that is, in a fixed duration), you should set the convergence precision as small as possible.

**Table 2** **response_data** parameter description
Parameter	Type	Description
pagerank	List	PageRank value of each vertex. The format is as follows: [{vertexId:rankValue},...], where vertexId is of the string type. rankValue is of the double type.

Parent topic: Algorithm API Parameter References

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