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Teleportation

As explained in Graph Algorithms for Data Science

Teleportation is the PageRank mechanism that assigns a probability of jumping from the current node to a randomly selected node instead of following an outgoing relationship.

Teleportation prevents PageRank from leaking when a node has no outgoing links. Instead of always following an outgoing relationship, it gives a small probability of jumping to a randomly selected node. section 5.4.1

PageRank combines two probabilities:

  • The damping factor is the probability of following an outgoing link; the typical value is 0.85. section 5.4.1
  • The remaining probability, 1 minus the damping factor, selects a node uniformly at random, including the current node. section 5.4.1

Followers network

A web surfer may follow links or jump elsewhere when bored. In a followers graph, PageRank uses the same mechanism to handle dead ends and rank influential users. section 5.4.1

Listing 5.12 Executing PageRank on the followers network
CALL gds.pageRank.write('follower-network',
 {writeProperty:'followerPageRank'})

Run PageRank on the projected followers network and write each result to a followerPageRank property. section 5.4.1

How Graph Algorithms for Data Science covers it

How deep it goes
Explains the jump probability, damping factor, uniform selection, and dead-end failure mode, but does not derive the full algorithm.
Examples
Uses a web-surfer analogy and a followers network executed with Cypher and GDS PageRank.
What it assumes
Assumes outgoing relationships and basic network-flow reasoning while teaching dead ends and probabilistic jumps.
What you can do after
Apply it via GDS PageRank to avoid rank leakage and inspect influence scores on a followers network.
How it connects
Connects PageRank with damping factor, outgoing links, dead ends, and network influence.

Related concepts

This explainer was generated by Manning's knowledge graph from the text of Graph Algorithms for Data Science. The section links point to the parts of the book it draws on.