Time-dependent Personalized PageRank for temporal networks: discrete and continuous scales

TL;DR

This paper investigates time-dependent personalized PageRank in temporal networks across discrete and continuous scales, highlighting the relationship between these settings and providing bounds on influence, supported by real and synthetic examples.

Contribution

It introduces a unified analysis of personalized PageRank on temporal networks for both discrete and continuous time, with new bounds on influence and illustrative examples.

Findings

Bounds on influence of personalization vectors

Unified framework for discrete and continuous scales

Validated results with real and synthetic data

Abstract

In this paper we explore the PageRank of temporal networks on both discrete and continuous time scales in the presence of personalization vectors that vary over time. Also the underlying interplay between the discrete and continuous settings arising from discretization is highlighted. Additionally, localization results that set bounds to the estimated influence of the personalization vector on the ranking of a particular node are given. The theoretical results are illustrated by means of some real and synthetic examples.