PageRank with priors : an influence propagation perspective

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review

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Author(s)

  • Biao Xiang
  • Qi Liu
  • Enhong Chen
  • Hui Xiong
  • Yi Zheng

Detail(s)

Original languageEnglish
Title of host publicationProceedings of the Twenty-Third International Joint Conference on Artificial Intelligence
EditorsFrancesca Rossi
PublisherAAAI Press/International Joint Conferences on Artificial Intelligence
Pages2740-2746
ISBN (Print)978-1-57735-633-2
Publication statusPublished - Aug 2013
Externally publishedYes

Publication series

NameIJCAI International Joint Conference on Artificial Intelligence
ISSN (Print)1045-0823

Conference

Title23rd International Joint Conference on Artificial Intelligence, IJCAI 2013
PlaceChina
CityBeijing
Period3 - 9 August 2013

Abstract

Recent years have witnessed increased interests in measuring authority and modelling influence in social networks. For a long time, PageRank has been widely used for authority computation and has also been adopted as a solid baseline for evaluating social influence related applications. However, the connection between authority measurement and influence modelling is not clearly established. To this end, in this paper, we provide a focused study on understanding of 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 is essentially PageRank with prior. Also, we show that the authority computation by PageRank can be enhanced with more generalized priors. Moreover, to deal with the computational challenge of PageRank with general priors, we provide an upper bound for top authoritative nodes identification. Finally, the experimental results on the scientific collaboration network validate the effectiveness of the proposed social influence model.

Citation Format(s)

PageRank with priors : an influence propagation perspective. / Xiang, Biao; Liu, Qi; Chen, Enhong et al.

Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence. ed. / Francesca Rossi. AAAI Press/International Joint Conferences on Artificial Intelligence, 2013. p. 2740-2746 (IJCAI International Joint Conference on Artificial Intelligence).

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review