### Abstract

Personalized PageRank is an algorithm to classify the importance 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 proportion of time a node is visited 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 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.

Original language | English |
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Title of host publication | Algorithms and Models for the Web Graph (11th International Workshop, WAW 2014, Beijing, China, December 17-18, 2014. Proceedings) |

Editors | A. Bonato, F.C. Graham, P. Pralat |

Place of Publication | Berlin |

Publisher | Springer |

Pages | 23-33 |

ISBN (Print) | 978-3-319-13122-1 |

DOIs | |

Publication status | Published - 2014 |

Event | conference; 11th International Workshop on Algorithms and Models for the Web Graph; 2014-12-17; 2014-12-18 - Duration: 17 Dec 2014 → 18 Dec 2014 |

### Publication series

Name | Lecture Notes in Computer Science |
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Volume | 8882 |

ISSN (Print) | 0302-9743 |

### Conference

Conference | conference; 11th International Workshop on Algorithms and Models for the Web Graph; 2014-12-17; 2014-12-18 |
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Period | 17/12/14 → 18/12/14 |

Other | 11th International Workshop on Algorithms and Models for the Web Graph |

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## Cite this

Avrachenkov, K. E., Hofstad, van der, R. W., & Sokol, M. (2014). Personalized PageRank with node-dependent restart. In A. Bonato, F. C. Graham, & P. Pralat (Eds.),

*Algorithms and Models for the Web Graph (11th International Workshop, WAW 2014, Beijing, China, December 17-18, 2014. Proceedings)*(pp. 23-33). (Lecture Notes in Computer Science; Vol. 8882). Springer. https://doi.org/10.1007/978-3-319-13123-8_3