Personalized PageRank with Node-dependent Restart

Konstantin Avrachenkov (1) , Remco van Der Hofstad (2) , Marina Sokol (1)

1 MAESTRO - Models for the performance analysis and the control of networks
2 Departement of Mathematics and Computer Science

Personalized PageRank is an algorithm to classify the improtance of web pages on a user-dependent basis. We introduce two generalizations of Personalized PageRank with node-dependent restart. The first generalization is based on the proportion of visits to nodes before the restart, whereas the second generalization is based on the probability of visited node just before the restart. In the original case of constant restart probability, the two measures coincide. We discuss interesting particular cases of restart probabilities and restart distributions. We show that the both generalizations of Personalized PageRank have an elegant expression connecting the so-called direct and reverse Personalized PageRanks that yield a symmetry property of these Personalized PageRanks.

Mots clés: Random Walk on Graph; PageRank; Node-dependant Restart Probability

Domaines: Réseaux et télécommunications [cs.NI]

Konstantin Avrachenkov, Remco van Der Hofstad, Marina Sokol. Personalized PageRank with Node-dependent Restart. [Research Report] RR-8570, Inria. 2014, pp.12. ⟨hal-01052482⟩