Module

analysis.page_rank

PageRank implementation for Bengal SSG.

Computes page importance scores using the iterative power method. Takes advantage of hashable pages for efficient graph operations.

The PageRank algorithm assigns importance scores based on:

  • Number of incoming links (popularity)
  • Importance of pages linking to it (authority)
  • Damping factor for random navigation (user behavior)

References:

  • Brin, S., & Page, L. (1998). The anatomy of a large-scale hypertextual web search engine. Computer networks and ISDN systems.
View source
2 Classes 1 Function

Classes

PageRankResults dataclass
Results from PageRank computation. Contains importance scores for all pages based on the link stru…
3

Results from PageRank computation.

Contains importance scores for all pages based on the link structure. Pages linked to by many important pages receive high scores.

Attributes

Name Type Description
scores dict[Page, float]

Map of pages to PageRank scores (normalized, sum to 1.0)
iterations int

Number of iterations until convergence
converged bool

Whether the algorithm converged within max_iterations
damping_factor float

Methods 3

get_top_pages
Get top-ranked pages.
1 list[tuple[Page, float]] 
def get_top_pages(self, limit: int = 20) -> list[tuple[Page, float]]

Get top-ranked pages.

Parameters 1
limit int

Number of pages to return
Returns

list[tuple[Page, float]] —

List of (page, score) tuples sorted by score descending

get_pages_above_percentile
Get pages above a certain percentile.
1 set[Page] 
def get_pages_above_percentile(self, percentile: int) -> set[Page]

Get pages above a certain percentile.

Parameters 1
percentile int

Percentile threshold (0-100)
Returns

set[Page] —

Set of pages above the threshold

get_score
Get PageRank score for a specific page.
1 float 
def get_score(self, page: Page) -> float

Get PageRank score for a specific page.

Parameters 1
page Page
Returns

float

PageRankCalculator
Compute PageRank scores for pages in a site graph. PageRank is a link analysis algorithm that assi…
3

Compute PageRank scores for pages in a site graph.

PageRank is a link analysis algorithm that assigns numerical weights to pages based on their link structure. Pages that are linked to by many important pages receive high scores.

The algorithm uses an iterative approach:

  1. Initialize all pages with equal probability (1/N)
  2. Iteratively update scores based on incoming links
  3. Continue until convergence or max iterations

Methods 2

compute
Compute PageRank scores for all pages.
2 PageRankResults 
def compute(self, seed_pages: set[Page] | None = None, personalized: bool = False) -> PageRankResults

Compute PageRank scores for all pages.

Parameters 2
seed_pages set[Page] | None

Optional set of pages for personalized PageRank Random jumps go only to these pages
personalized bool

If True, use personalized PageRank
Returns

PageRankResults —

PageRankResults with scores and metadata

compute_personalized
Compute personalized PageRank from seed pages. Personalized PageRank biases ra…
1 PageRankResults 
def compute_personalized(self, seed_pages: set[Page]) -> PageRankResults

Compute personalized PageRank from seed pages.

Personalized PageRank biases random jumps toward seed pages, useful for finding pages related to a specific topic.

Parameters 1
seed_pages set[Page]

Set of pages to bias toward
Returns

PageRankResults —

PageRankResults with personalized scores

Internal Methods 1
__init__
Initialize PageRank calculator.
4 None 
def __init__(self, graph: KnowledgeGraph, damping: float = 0.85, max_iterations: int = 100, convergence_threshold: float = 1e-06)

Initialize PageRank calculator.

Parameters 4
graph KnowledgeGraph

KnowledgeGraph with page connections
damping float

Probability of following links vs random jump (0-1) Default 0.85 means 85% follow links, 15% random jump
max_iterations int

Maximum iterations before stopping
convergence_threshold float

Stop when max score change < this value

Functions

analyze_page_importance
Convenience function to analyze page importance.
3 list[tuple[Page, float]] 
def analyze_page_importance(graph: KnowledgeGraph, damping: float = 0.85, top_n: int = 20) -> list[tuple[Page, float]]

Convenience function to analyze page importance.

Parameters 3

Name Type Default Description
graph KnowledgeGraph —

KnowledgeGraph with page connections
damping float 0.85

Damping factor (default 0.85)
top_n int 20

Number of top pages to return

Returns

list[tuple[Page, float]] —

List of (page, score) tuples for top N pages