An Efficient Alternative to Personalized Page Rank for Friend Recommendations
Document Type
Conference Proceeding
Publication Date
3-19-2018
Publication Title
Consumer Communications & Networking Conference (CCNC), 2018 15th IEEE Annual
Publisher
IEEE
First page number:
1
Last page number:
2
Abstract
In this paper, a new algorithm is proposed to calculate the value of friend score in a graph. The main goal is decreasing the running time and accuracy of calculation. Even though similar estimations have been done, the proposed method is faster because it increases the speed of computation with ignoring calculation of not involved nodes. This feature makes this method an acceptable candidate for large networks analyses like search engines and social media friend recommendation techniques. Based on the experiments done in this study, the FOF algorithm increases the speed of calculation by a factor of 28 on the two test datasets, namely, Amazon and Facebook.
Keywords
Friend recommendation; Online social networks; Page rank; Personalized page rank
Disciplines
Electrical and Computer Engineering
Language
English
Repository Citation
Zhan, F.,
Waters, B.,
Mijangos, M.,
Chung, L.,
Bhagat, R.,
Bhagat, T.,
Pirouz, M.,
Chiu, C.,
Tayeb, S.,
Ploutz, E.,
Zhan, J.,
Gewali, L.
(2018).
An Efficient Alternative to Personalized Page Rank for Friend Recommendations.
Consumer Communications & Networking Conference (CCNC), 2018 15th IEEE Annual
1-2.
IEEE.
http://dx.doi.org/10.1109/CCNC.2018.8319307