pagerank

hana_ml.algorithms.pal.pagerank.pagerank(data, damping=None, max_iter=None, tol=None, thread_ratio=None)

PageRank is an algorithm used by a search engine to measure the importance of website pages. A website page is considered more important if it receives more links from other websites. PageRank represents the likelihood that a visitor will visit a particular page by randomly clicking of other webpages. Higher rank in PageRank means greater probability of the site being reached.

Parameters

dataDataFrame

Data for predicting the class labels.

dampingfloat, optional

The damping factor d.

Defaults to 0.85.

max_iterint, optional

The maximum number of iterations of power method. The value 0 means no maximum number of iterations is set and the calculation stops when the result converges.

Defaults to 0.

tolfloat, optional

Specifies the stop condition. When the mean improvement value of ranks is less than this value, the program stops calculation.

Defaults to 1e-6.

thread_ratiofloat, optional

Adjusts the percentage of available threads to use, from 0 to 1. A value of 0 indicates the use of a single thread, while 1 implies the use of all possible current threads. Values outside the range will be ignored and this function heuristically determines the number of threads to use.

Defaults to 0.

Returns

DataFrame

Calculated rank values and corresponding node names.