PersonalizedPageRankAnchorSearcher

class PersonalizedPageRankAnchorSearcher(batch_size=1, use_tqdm=False, page_rank_kwargs=None)

Bases: AnchorSearcher

Select closest anchors as the nodes with the largest personalized page rank.

See also

http://web.stanford.edu/class/cs224w/slides/04-pagerank.pdf

Initialize the searcher.

Parameters:

• batch_size (int) – the batch size to use.

• use_tqdm (bool) – whether to use tqdm

• page_rank_kwargs (Optional[Mapping[str, Any]]) – keyword-based parameters used for page_rank(). Must not include edge_index, or x0.

Methods Summary

__call__(edge_index, anchors, k[, num_entities])	Find the \(k\) closest anchor nodes for each entity.
iter_extra_repr()	Iterate over the components of the extra_repr().
precalculate_anchor_ppr(edge_index, anchors)	Sort anchors nodes by PPR values from each node.

Methods Documentation

__call__(edge_index, anchors, k, num_entities=None)

Find the \(k\) closest anchor nodes for each entity.

Parameters:

• edge_index (ndarray) – shape: (2, m) the edge index

• anchors (ndarray) – shape: (a,) the selected anchor entity Ids

• k (int) – the number of closest anchors to return

• num_entities (Optional[int]) – the number of entities

Return type:

ndarray

Returns:

shape: (n, k), -1 <= res < a the Ids of the closest anchors

iter_extra_repr()

Iterate over the components of the extra_repr().

This method is typically overridden. A common pattern would be

def iter_extra_repr(self) -> Iterable[str]:
    yield from super().iter_extra_repr()
    yield "<key1>=<value1>"
    yield "<key2>=<value2>"

Return type:

Iterable[str]

Returns:

an iterable over individual components of the extra_repr()

precalculate_anchor_ppr(edge_index, anchors)

Sort anchors nodes by PPR values from each node.

Parameters:

• edge_index (ndarray) – shape: (2, m) the edge index.

• anchors (ndarray) – shape: (num_anchors,) the anchor IDs.

Return type:

ndarray

Returns:

shape: (num_entities, num_anchors) the PPR values for each anchor