An Influence Propagation View of PageRank

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

69 Scopus Citations
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Author(s)

  • Qi LIU
  • Biao XIANG
  • Nicholas Jing YUAN
  • Enhong CHEN
  • Hui XIONG
  • Yi ZHENG

Detail(s)

Original languageEnglish
Article number30
Journal / PublicationACM Transactions on Knowledge Discovery from Data
Volume11
Issue number3
Online publishedMar 2017
Publication statusPublished - 2017
Externally publishedYes

Abstract

For a long time, PageRank has been widely used for authority computation and has been adopted as a solid baseline for evaluating social influence related applications. However, when measuring the authority of network nodes, the traditional PageRank method does not take the nodes' prior knowledge into consideration. Also, the connection between PageRank and social influence modeling methods is not clearly established. To that end, this article provides a focused study on understanding PageRank as well as the relationship between PageRank and social influence analysis. Along this line, we first propose a linear social influence model and reveal that this model generalizes the PageRank-based authority computation by introducing some constraints. Then, we show that the authority computation by PageRank can be enhanced if exploiting more reasonable constraints (e.g., from prior knowledge). Next, to deal with the computational challenge of linear model with general constraints, we provide an upper bound for identifying nodes with top authorities. Moreover, we extend the proposed linear model for better measuring the authority of the given node sets, and we also demonstrate the way to quickly identify the top authoritative node sets. Finally, extensive experimental evaluations on four real-world networks validate the effectiveness of the proposed linear model with respect to different constraint settings. The results show that the methods with more reasonable constraints can lead to better ranking and recommendation performance. Meanwhile, the upper bounds formed by PageRank values could be used to quickly locate the nodes and node sets with the highest authorities.

Research Area(s)

  • Authority, PageRank, Priors, Social influence propagation, Upper bounds

Citation Format(s)

An Influence Propagation View of PageRank. / LIU, Qi; XIANG, Biao; YUAN, Nicholas Jing et al.

In: ACM Transactions on Knowledge Discovery from Data, Vol. 11, No. 3, 30, 2017.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review